{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "FinRL_stock_trading_NeurIPS_2018.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "uijiWgkuh1jB",
        "MRiOtrywfAo1",
        "_gDkU-j-fCmZ",
        "3Zpv4S0-fDBv"
      ],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.12"
    },
    "pycharm": {
      "stem_cell": {
        "cell_type": "raw",
        "metadata": {
          "collapsed": false
        },
        "source": []
      }
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/AI4Finance-LLC/FinRL-Library/blob/master/FinRL_stock_trading_NeurIPS_2018.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gXaoZs2lh1hi"
      },
      "source": [
        "# Deep Reinforcement Learning for Stock Trading from Scratch: Multiple Stock Trading\n",
        "\n",
        "Tutorials to use OpenAI DRL to trade multiple stocks in one Jupyter Notebook | Presented at NeurIPS 2020: Deep RL Workshop\n",
        "\n",
        "* This blog uses FinRL to reproduce the paper: Practical Deep Reinforcement Learning Approach for Stock Trading, Workshop on Challenges and Opportunities for AI in Financial Services, NeurIPS 2018.\n",
        "* Check out medium blog for detailed explanations: https://towardsdatascience.com/finrl-for-quantitative-finance-tutorial-for-multiple-stock-trading-7b00763b7530\n",
        "* Please report any issues to our Github: https://github.com/AI4Finance-LLC/FinRL-Library/issues\n",
        "* **Pytorch Version** \n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lGunVt8oLCVS"
      },
      "source": [
        "# Content"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HOzAKQ-SLGX6"
      },
      "source": [
        "* [1. Problem Definition](#0)\n",
        "* [2. Getting Started - Load Python packages](#1)\n",
        "    * [2.1. Install Packages](#1.1)    \n",
        "    * [2.2. Check Additional Packages](#1.2)\n",
        "    * [2.3. Import Packages](#1.3)\n",
        "    * [2.4. Create Folders](#1.4)\n",
        "* [3. Download Data](#2)\n",
        "* [4. Preprocess Data](#3)        \n",
        "    * [4.1. Technical Indicators](#3.1)\n",
        "    * [4.2. Perform Feature Engineering](#3.2)\n",
        "* [5.Build Environment](#4)  \n",
        "    * [5.1. Training & Trade Data Split](#4.1)\n",
        "    * [5.2. User-defined Environment](#4.2)   \n",
        "    * [5.3. Initialize Environment](#4.3)    \n",
        "* [6.Implement DRL Algorithms](#5)  \n",
        "* [7.Backtesting Performance](#6)  \n",
        "    * [7.1. BackTestStats](#6.1)\n",
        "    * [7.2. BackTestPlot](#6.2)   \n",
        "    * [7.3. Baseline Stats](#6.3)   \n",
        "    * [7.3. Compare to Stock Market Index](#6.4)             "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sApkDlD9LIZv"
      },
      "source": [
        "<a id='0'></a>\n",
        "# Part 1. Problem Definition"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HjLD2TZSLKZ-"
      },
      "source": [
        "This problem is to design an automated trading solution for single stock trading. We model the stock trading process as a Markov Decision Process (MDP). We then formulate our trading goal as a maximization problem.\n",
        "\n",
        "The algorithm is trained using Deep Reinforcement Learning (DRL) algorithms and the components of the reinforcement learning environment are:\n",
        "\n",
        "\n",
        "* Action: The action space describes the allowed actions that the agent interacts with the\n",
        "environment. Normally, a ∈ A includes three actions: a ∈ {−1, 0, 1}, where −1, 0, 1 represent\n",
        "selling, holding, and buying one stock. Also, an action can be carried upon multiple shares. We use\n",
        "an action space {−k, ..., −1, 0, 1, ..., k}, where k denotes the number of shares. For example, \"Buy\n",
        "10 shares of AAPL\" or \"Sell 10 shares of AAPL\" are 10 or −10, respectively\n",
        "\n",
        "* Reward function: r(s, a, s′) is the incentive mechanism for an agent to learn a better action. The change of the portfolio value when action a is taken at state s and arriving at new state s',  i.e., r(s, a, s′) = v′ − v, where v′ and v represent the portfolio\n",
        "values at state s′ and s, respectively\n",
        "\n",
        "* State: The state space describes the observations that the agent receives from the environment. Just as a human trader needs to analyze various information before executing a trade, so\n",
        "our trading agent observes many different features to better learn in an interactive environment.\n",
        "\n",
        "* Environment: Dow 30 consituents\n",
        "\n",
        "\n",
        "The data of the single stock that we will be using for this case study is obtained from Yahoo Finance API. The data contains Open-High-Low-Close price and volume.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ffsre789LY08"
      },
      "source": [
        "<a id='1'></a>\n",
        "# Part 2. Getting Started- Load Python Packages"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Uy5_PTmOh1hj"
      },
      "source": [
        "<a id='1.1'></a>\n",
        "## 2.1. Install all the packages through FinRL library\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mPT0ipYE28wL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4a130fa7-6727-4a17-89ba-855191887f81"
      },
      "source": [
        "## install finrl library\n",
        "!pip install git+https://github.com/AI4Finance-LLC/FinRL-Library.git"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting git+https://github.com/AI4Finance-LLC/FinRL-Library.git\n",
            "  Cloning https://github.com/AI4Finance-LLC/FinRL-Library.git to /tmp/pip-req-build-nve33bms\n",
            "  Running command git clone -q https://github.com/AI4Finance-LLC/FinRL-Library.git /tmp/pip-req-build-nve33bms\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from finrl==0.0.3) (1.19.5)\n",
            "Requirement already satisfied: pandas>=1.1.5 in /usr/local/lib/python3.7/dist-packages (from finrl==0.0.3) (1.1.5)\n",
            "Collecting stockstats\n",
            "  Downloading https://files.pythonhosted.org/packages/32/41/d3828c5bc0a262cb3112a4024108a3b019c183fa3b3078bff34bf25abf91/stockstats-0.3.2-py2.py3-none-any.whl\n",
            "Collecting yfinance\n",
            "  Downloading https://files.pythonhosted.org/packages/7a/e8/b9d7104d3a4bf39924799067592d9e59119fcfc900a425a12e80a3123ec8/yfinance-0.1.55.tar.gz\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from finrl==0.0.3) (3.2.2)\n",
            "Requirement already satisfied: scikit-learn>=0.21.0 in /usr/local/lib/python3.7/dist-packages (from finrl==0.0.3) (0.22.2.post1)\n",
            "Requirement already satisfied: gym>=0.17 in /usr/local/lib/python3.7/dist-packages (from finrl==0.0.3) (0.17.3)\n",
            "Collecting stable-baselines3[extra]\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/76/7c/ec89fd9a51c2ff640f150479069be817136c02f02349b5dd27a6e3bb8b3d/stable_baselines3-0.10.0-py3-none-any.whl (145kB)\n",
            "\u001b[K     |████████████████████████████████| 153kB 7.0MB/s \n",
            "\u001b[?25hRequirement already satisfied: pytest in /usr/local/lib/python3.7/dist-packages (from finrl==0.0.3) (3.6.4)\n",
            "Requirement already satisfied: setuptools>=41.4.0 in /usr/local/lib/python3.7/dist-packages (from finrl==0.0.3) (53.0.0)\n",
            "Requirement already satisfied: wheel>=0.33.6 in /usr/local/lib/python3.7/dist-packages (from finrl==0.0.3) (0.36.2)\n",
            "Collecting pyfolio@ git+https://github.com/quantopian/pyfolio.git#egg=pyfolio-0.9.2\n",
            "  Cloning https://github.com/quantopian/pyfolio.git to /tmp/pip-install-9t1a17l_/pyfolio\n",
            "  Running command git clone -q https://github.com/quantopian/pyfolio.git /tmp/pip-install-9t1a17l_/pyfolio\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas>=1.1.5->finrl==0.0.3) (2018.9)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=1.1.5->finrl==0.0.3) (2.8.1)\n",
            "Collecting int-date>=0.1.7\n",
            "  Downloading https://files.pythonhosted.org/packages/43/27/31803df15173ab341fe7548c14154b54227dfd8f630daa09a1c6e7db52f7/int_date-0.1.8-py2.py3-none-any.whl\n",
            "Requirement already satisfied: requests>=2.20 in /usr/local/lib/python3.7/dist-packages (from yfinance->finrl==0.0.3) (2.23.0)\n",
            "Requirement already satisfied: multitasking>=0.0.7 in /usr/local/lib/python3.7/dist-packages (from yfinance->finrl==0.0.3) (0.0.9)\n",
            "Collecting lxml>=4.5.1\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/d2/88/b25778f17e5320c1c58f8c5060fb5b037288e162bd7554c30799e9ea90db/lxml-4.6.2-cp37-cp37m-manylinux1_x86_64.whl (5.5MB)\n",
            "\u001b[K     |████████████████████████████████| 5.5MB 34.4MB/s \n",
            "\u001b[?25hRequirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->finrl==0.0.3) (0.10.0)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->finrl==0.0.3) (2.4.7)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->finrl==0.0.3) (1.3.1)\n",
            "Requirement already satisfied: scipy>=0.17.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.21.0->finrl==0.0.3) (1.4.1)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.21.0->finrl==0.0.3) (1.0.1)\n",
            "Requirement already satisfied: pyglet<=1.5.0,>=1.4.0 in /usr/local/lib/python3.7/dist-packages (from gym>=0.17->finrl==0.0.3) (1.5.0)\n",
            "Requirement already satisfied: cloudpickle<1.7.0,>=1.2.0 in /usr/local/lib/python3.7/dist-packages (from gym>=0.17->finrl==0.0.3) (1.3.0)\n",
            "Requirement already satisfied: torch>=1.4.0 in /usr/local/lib/python3.7/dist-packages (from stable-baselines3[extra]->finrl==0.0.3) (1.7.0+cu101)\n",
            "Requirement already satisfied: tensorboard; extra == \"extra\" in /usr/local/lib/python3.7/dist-packages (from stable-baselines3[extra]->finrl==0.0.3) (2.4.1)\n",
            "Requirement already satisfied: pillow; extra == \"extra\" in /usr/local/lib/python3.7/dist-packages (from stable-baselines3[extra]->finrl==0.0.3) (7.0.0)\n",
            "Requirement already satisfied: atari-py~=0.2.0; extra == \"extra\" in /usr/local/lib/python3.7/dist-packages (from stable-baselines3[extra]->finrl==0.0.3) (0.2.6)\n",
            "Requirement already satisfied: opencv-python; extra == \"extra\" in /usr/local/lib/python3.7/dist-packages (from stable-baselines3[extra]->finrl==0.0.3) (4.1.2.30)\n",
            "Requirement already satisfied: psutil; extra == \"extra\" in /usr/local/lib/python3.7/dist-packages (from stable-baselines3[extra]->finrl==0.0.3) (5.4.8)\n",
            "Requirement already satisfied: atomicwrites>=1.0 in /usr/local/lib/python3.7/dist-packages (from pytest->finrl==0.0.3) (1.4.0)\n",
            "Requirement already satisfied: more-itertools>=4.0.0 in /usr/local/lib/python3.7/dist-packages (from pytest->finrl==0.0.3) (8.7.0)\n",
            "Requirement already satisfied: py>=1.5.0 in /usr/local/lib/python3.7/dist-packages (from pytest->finrl==0.0.3) (1.10.0)\n",
            "Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.7/dist-packages (from pytest->finrl==0.0.3) (1.15.0)\n",
            "Requirement already satisfied: attrs>=17.4.0 in /usr/local/lib/python3.7/dist-packages (from pytest->finrl==0.0.3) (20.3.0)\n",
            "Requirement already satisfied: pluggy<0.8,>=0.5 in /usr/local/lib/python3.7/dist-packages (from pytest->finrl==0.0.3) (0.7.1)\n",
            "Requirement already satisfied: ipython>=3.2.3 in /usr/local/lib/python3.7/dist-packages (from pyfolio@ git+https://github.com/quantopian/pyfolio.git#egg=pyfolio-0.9.2->finrl==0.0.3) (5.5.0)\n",
            "Requirement already satisfied: seaborn>=0.7.1 in /usr/local/lib/python3.7/dist-packages (from pyfolio@ git+https://github.com/quantopian/pyfolio.git#egg=pyfolio-0.9.2->finrl==0.0.3) (0.11.1)\n",
            "Collecting empyrical>=0.5.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/74/43/1b997c21411c6ab7c96dc034e160198272c7a785aeea7654c9bcf98bec83/empyrical-0.5.5.tar.gz (52kB)\n",
            "\u001b[K     |████████████████████████████████| 61kB 8.8MB/s \n",
            "\u001b[?25hRequirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.20->yfinance->finrl==0.0.3) (1.24.3)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests>=2.20->yfinance->finrl==0.0.3) (3.0.4)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.20->yfinance->finrl==0.0.3) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.20->yfinance->finrl==0.0.3) (2020.12.5)\n",
            "Requirement already satisfied: future in /usr/local/lib/python3.7/dist-packages (from pyglet<=1.5.0,>=1.4.0->gym>=0.17->finrl==0.0.3) (0.16.0)\n",
            "Requirement already satisfied: dataclasses in /usr/local/lib/python3.7/dist-packages (from torch>=1.4.0->stable-baselines3[extra]->finrl==0.0.3) (0.6)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch>=1.4.0->stable-baselines3[extra]->finrl==0.0.3) (3.7.4.3)\n",
            "Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard; extra == \"extra\"->stable-baselines3[extra]->finrl==0.0.3) (1.8.0)\n",
            "Requirement already satisfied: grpcio>=1.24.3 in /usr/local/lib/python3.7/dist-packages (from tensorboard; extra == \"extra\"->stable-baselines3[extra]->finrl==0.0.3) (1.32.0)\n",
            "Requirement already satisfied: absl-py>=0.4 in /usr/local/lib/python3.7/dist-packages (from tensorboard; extra == \"extra\"->stable-baselines3[extra]->finrl==0.0.3) (0.10.0)\n",
            "Requirement already satisfied: google-auth<2,>=1.6.3 in /usr/local/lib/python3.7/dist-packages (from tensorboard; extra == \"extra\"->stable-baselines3[extra]->finrl==0.0.3) (1.27.0)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.7/dist-packages (from tensorboard; extra == \"extra\"->stable-baselines3[extra]->finrl==0.0.3) (3.3.3)\n",
            "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.7/dist-packages (from tensorboard; extra == \"extra\"->stable-baselines3[extra]->finrl==0.0.3) (1.0.1)\n",
            "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.7/dist-packages (from tensorboard; extra == \"extra\"->stable-baselines3[extra]->finrl==0.0.3) (0.4.2)\n",
            "Requirement already satisfied: protobuf>=3.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard; extra == \"extra\"->stable-baselines3[extra]->finrl==0.0.3) (3.12.4)\n",
            "Requirement already satisfied: traitlets>=4.2 in /usr/local/lib/python3.7/dist-packages (from ipython>=3.2.3->pyfolio@ git+https://github.com/quantopian/pyfolio.git#egg=pyfolio-0.9.2->finrl==0.0.3) (4.3.3)\n",
            "Requirement already satisfied: pickleshare in /usr/local/lib/python3.7/dist-packages (from ipython>=3.2.3->pyfolio@ git+https://github.com/quantopian/pyfolio.git#egg=pyfolio-0.9.2->finrl==0.0.3) (0.7.5)\n",
            "Requirement already satisfied: pygments in /usr/local/lib/python3.7/dist-packages (from ipython>=3.2.3->pyfolio@ git+https://github.com/quantopian/pyfolio.git#egg=pyfolio-0.9.2->finrl==0.0.3) (2.6.1)\n",
            "Requirement already satisfied: decorator in /usr/local/lib/python3.7/dist-packages (from ipython>=3.2.3->pyfolio@ git+https://github.com/quantopian/pyfolio.git#egg=pyfolio-0.9.2->finrl==0.0.3) (4.4.2)\n",
            "Requirement already satisfied: simplegeneric>0.8 in /usr/local/lib/python3.7/dist-packages (from ipython>=3.2.3->pyfolio@ git+https://github.com/quantopian/pyfolio.git#egg=pyfolio-0.9.2->finrl==0.0.3) (0.8.1)\n",
            "Requirement already satisfied: pexpect; sys_platform != \"win32\" in /usr/local/lib/python3.7/dist-packages (from ipython>=3.2.3->pyfolio@ git+https://github.com/quantopian/pyfolio.git#egg=pyfolio-0.9.2->finrl==0.0.3) (4.8.0)\n",
            "Requirement already satisfied: prompt-toolkit<2.0.0,>=1.0.4 in /usr/local/lib/python3.7/dist-packages (from ipython>=3.2.3->pyfolio@ git+https://github.com/quantopian/pyfolio.git#egg=pyfolio-0.9.2->finrl==0.0.3) (1.0.18)\n",
            "Requirement already satisfied: pandas-datareader>=0.2 in /usr/local/lib/python3.7/dist-packages (from empyrical>=0.5.0->pyfolio@ git+https://github.com/quantopian/pyfolio.git#egg=pyfolio-0.9.2->finrl==0.0.3) (0.9.0)\n",
            "Requirement already satisfied: rsa<5,>=3.1.4; python_version >= \"3.6\" in /usr/local/lib/python3.7/dist-packages (from google-auth<2,>=1.6.3->tensorboard; extra == \"extra\"->stable-baselines3[extra]->finrl==0.0.3) (4.7.1)\n",
            "Requirement already satisfied: cachetools<5.0,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from google-auth<2,>=1.6.3->tensorboard; extra == \"extra\"->stable-baselines3[extra]->finrl==0.0.3) (4.2.1)\n",
            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.7/dist-packages (from google-auth<2,>=1.6.3->tensorboard; extra == \"extra\"->stable-baselines3[extra]->finrl==0.0.3) (0.2.8)\n",
            "Requirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from markdown>=2.6.8->tensorboard; extra == \"extra\"->stable-baselines3[extra]->finrl==0.0.3) (3.4.0)\n",
            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.7/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard; extra == \"extra\"->stable-baselines3[extra]->finrl==0.0.3) (1.3.0)\n",
            "Requirement already satisfied: ipython-genutils in /usr/local/lib/python3.7/dist-packages (from traitlets>=4.2->ipython>=3.2.3->pyfolio@ git+https://github.com/quantopian/pyfolio.git#egg=pyfolio-0.9.2->finrl==0.0.3) (0.2.0)\n",
            "Requirement already satisfied: ptyprocess>=0.5 in /usr/local/lib/python3.7/dist-packages (from pexpect; sys_platform != \"win32\"->ipython>=3.2.3->pyfolio@ git+https://github.com/quantopian/pyfolio.git#egg=pyfolio-0.9.2->finrl==0.0.3) (0.7.0)\n",
            "Requirement already satisfied: wcwidth in /usr/local/lib/python3.7/dist-packages (from prompt-toolkit<2.0.0,>=1.0.4->ipython>=3.2.3->pyfolio@ git+https://github.com/quantopian/pyfolio.git#egg=pyfolio-0.9.2->finrl==0.0.3) (0.2.5)\n",
            "Requirement already satisfied: pyasn1>=0.1.3 in /usr/local/lib/python3.7/dist-packages (from rsa<5,>=3.1.4; python_version >= \"3.6\"->google-auth<2,>=1.6.3->tensorboard; extra == \"extra\"->stable-baselines3[extra]->finrl==0.0.3) (0.4.8)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->markdown>=2.6.8->tensorboard; extra == \"extra\"->stable-baselines3[extra]->finrl==0.0.3) (3.4.0)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard; extra == \"extra\"->stable-baselines3[extra]->finrl==0.0.3) (3.1.0)\n",
            "Building wheels for collected packages: finrl, yfinance, pyfolio, empyrical\n",
            "  Building wheel for finrl (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for finrl: filename=finrl-0.0.3-cp37-none-any.whl size=38102 sha256=e5a13fc3501b6a9b63cba256f9e6ea6a9f6da2304e072cb722ab0ce62292c010\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-5lyanf9f/wheels/9c/19/bf/c644def96612df1ad42c94d5304966797eaa3221dffc5efe0b\n",
            "  Building wheel for yfinance (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for yfinance: filename=yfinance-0.1.55-py2.py3-none-any.whl size=22616 sha256=ce296cbb197f918847b53c96556ac58bbfa0f15e1203984ac8c428bf9f4b1966\n",
            "  Stored in directory: /root/.cache/pip/wheels/04/98/cc/2702a4242d60bdc14f48b4557c427ded1fe92aedf257d4565c\n",
            "  Building wheel for pyfolio (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pyfolio: filename=pyfolio-0.9.2+75.g4b901f6-cp37-none-any.whl size=75764 sha256=495b5efa7abb45cf78c48725fed0f4948cef08d8f396ffab4f9c3f0639151cec\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-5lyanf9f/wheels/43/ce/d9/6752fb6e03205408773235435205a0519d2c608a94f1976e56\n",
            "  Building wheel for empyrical (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for empyrical: filename=empyrical-0.5.5-cp37-none-any.whl size=39764 sha256=64a1eba99b8483a5e6d86d29b8d34168906847c70eedc8b9c36ecddf9f4723d8\n",
            "  Stored in directory: /root/.cache/pip/wheels/ea/b2/c8/6769d8444d2f2e608fae2641833110668d0ffd1abeb2e9f3fc\n",
            "Successfully built finrl yfinance pyfolio empyrical\n",
            "Installing collected packages: int-date, stockstats, lxml, yfinance, stable-baselines3, empyrical, pyfolio, finrl\n",
            "  Found existing installation: lxml 4.2.6\n",
            "    Uninstalling lxml-4.2.6:\n",
            "      Successfully uninstalled lxml-4.2.6\n",
            "Successfully installed empyrical-0.5.5 finrl-0.0.3 int-date-0.1.8 lxml-4.6.2 pyfolio-0.9.2+75.g4b901f6 stable-baselines3-0.10.0 stockstats-0.3.2 yfinance-0.1.55\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "osBHhVysOEzi"
      },
      "source": [
        "\n",
        "<a id='1.2'></a>\n",
        "## 2.2. Check if the additional packages needed are present, if not install them. \n",
        "* Yahoo Finance API\n",
        "* pandas\n",
        "* numpy\n",
        "* matplotlib\n",
        "* stockstats\n",
        "* OpenAI gym\n",
        "* stable-baselines\n",
        "* tensorflow\n",
        "* pyfolio"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nGv01K8Sh1hn"
      },
      "source": [
        "<a id='1.3'></a>\n",
        "## 2.3. Import Packages"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lPqeTTwoh1hn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b5fdebb3-b7d0-4fe6-b993-fc4c1efc51ff"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "# matplotlib.use('Agg')\n",
        "import datetime\n",
        "\n",
        "%matplotlib inline\n",
        "from finrl.config import config\n",
        "from finrl.marketdata.yahoodownloader import YahooDownloader\n",
        "from finrl.preprocessing.preprocessors import FeatureEngineer\n",
        "from finrl.preprocessing.data import data_split\n",
        "from finrl.env.env_stocktrading import StockTradingEnv\n",
        "from finrl.model.models import DRLAgent\n",
        "from finrl.trade.backtest import backtest_stats, backtest_plot, get_daily_return, get_baseline\n",
        "\n",
        "from pprint import pprint\n",
        "\n",
        "import sys\n",
        "sys.path.append(\"../FinRL-Library\")\n",
        "\n",
        "import itertools"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/pyfolio/pos.py:27: UserWarning: Module \"zipline.assets\" not found; multipliers will not be applied to position notionals.\n",
            "  'Module \"zipline.assets\" not found; multipliers will not be applied'\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T2owTj985RW4"
      },
      "source": [
        "<a id='1.4'></a>\n",
        "## 2.4. Create Folders"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w9A8CN5R5PuZ"
      },
      "source": [
        "import os\n",
        "if not os.path.exists(\"./\" + config.DATA_SAVE_DIR):\n",
        "    os.makedirs(\"./\" + config.DATA_SAVE_DIR)\n",
        "if not os.path.exists(\"./\" + config.TRAINED_MODEL_DIR):\n",
        "    os.makedirs(\"./\" + config.TRAINED_MODEL_DIR)\n",
        "if not os.path.exists(\"./\" + config.TENSORBOARD_LOG_DIR):\n",
        "    os.makedirs(\"./\" + config.TENSORBOARD_LOG_DIR)\n",
        "if not os.path.exists(\"./\" + config.RESULTS_DIR):\n",
        "    os.makedirs(\"./\" + config.RESULTS_DIR)"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A289rQWMh1hq"
      },
      "source": [
        "<a id='2'></a>\n",
        "# Part 3. Download Data\n",
        "Yahoo Finance is a website that provides stock data, financial news, financial reports, etc. All the data provided by Yahoo Finance is free.\n",
        "* FinRL uses a class **YahooDownloader** to fetch data from Yahoo Finance API\n",
        "* Call Limit: Using the Public API (without authentication), you are limited to 2,000 requests per hour per IP (or up to a total of 48,000 requests a day).\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NPeQ7iS-LoMm"
      },
      "source": [
        "\n",
        "\n",
        "-----\n",
        "class YahooDownloader:\n",
        "    Provides methods for retrieving daily stock data from\n",
        "    Yahoo Finance API\n",
        "\n",
        "    Attributes\n",
        "    ----------\n",
        "        start_date : str\n",
        "            start date of the data (modified from config.py)\n",
        "        end_date : str\n",
        "            end date of the data (modified from config.py)\n",
        "        ticker_list : list\n",
        "            a list of stock tickers (modified from config.py)\n",
        "\n",
        "    Methods\n",
        "    -------\n",
        "    fetch_data()\n",
        "        Fetches data from yahoo API\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "h3XJnvrbLp-C",
        "outputId": "90ad1901-fda9-46f8-80f6-083cd8d6293a"
      },
      "source": [
        "# from config.py start_date is a string\n",
        "config.START_DATE"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'2000-01-01'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "FUnY8WEfLq3C",
        "outputId": "9bd12370-a5e9-4eb3-bc2a-d0d0610f14f7"
      },
      "source": [
        "# from config.py end_date is a string\n",
        "config.END_DATE"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'2021-01-01'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JzqRRTOX6aFu",
        "outputId": "8acb257f-171e-4069-8ed3-1e7311ffecc2"
      },
      "source": [
        "print(config.DOW_30_TICKER)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['AAPL', 'MSFT', 'JPM', 'V', 'RTX', 'PG', 'GS', 'NKE', 'DIS', 'AXP', 'HD', 'INTC', 'WMT', 'IBM', 'MRK', 'UNH', 'KO', 'CAT', 'TRV', 'JNJ', 'CVX', 'MCD', 'VZ', 'CSCO', 'XOM', 'BA', 'MMM', 'PFE', 'WBA', 'DD']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yCKm4om-s9kE",
        "outputId": "2ba50005-317e-4fa0-d31a-70705b58d88a"
      },
      "source": [
        "df = YahooDownloader(start_date = '2009-01-01',\n",
        "                     end_date = '2021-01-01',\n",
        "                     ticker_list = config.DOW_30_TICKER).fetch_data()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (90630, 8)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CV3HrZHLh1hy",
        "outputId": "a0fc1d82-5e09-4ddc-b624-24f304db7f79"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(90630, 8)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "4hYkeaPiICHS",
        "outputId": "45c1f6a6-d7ef-4278-f0a6-1182f4c02189"
      },
      "source": [
        "df.sort_values(['date','tic'],ignore_index=True).head()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>close</th>\n",
              "      <th>volume</th>\n",
              "      <th>tic</th>\n",
              "      <th>day</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>3.067143</td>\n",
              "      <td>3.251429</td>\n",
              "      <td>3.041429</td>\n",
              "      <td>2.791740</td>\n",
              "      <td>746015200</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>18.570000</td>\n",
              "      <td>19.520000</td>\n",
              "      <td>18.400000</td>\n",
              "      <td>15.745411</td>\n",
              "      <td>10955700</td>\n",
              "      <td>AXP</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>42.799999</td>\n",
              "      <td>45.560001</td>\n",
              "      <td>42.779999</td>\n",
              "      <td>33.941101</td>\n",
              "      <td>7010200</td>\n",
              "      <td>BA</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>44.910000</td>\n",
              "      <td>46.980000</td>\n",
              "      <td>44.709999</td>\n",
              "      <td>32.978760</td>\n",
              "      <td>7117200</td>\n",
              "      <td>CAT</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>16.410000</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>16.250000</td>\n",
              "      <td>12.683227</td>\n",
              "      <td>40980600</td>\n",
              "      <td>CSCO</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         date       open       high        low      close     volume   tic  day\n",
              "0  2009-01-02   3.067143   3.251429   3.041429   2.791740  746015200  AAPL    4\n",
              "1  2009-01-02  18.570000  19.520000  18.400000  15.745411   10955700   AXP    4\n",
              "2  2009-01-02  42.799999  45.560001  42.779999  33.941101    7010200    BA    4\n",
              "3  2009-01-02  44.910000  46.980000  44.709999  32.978760    7117200   CAT    4\n",
              "4  2009-01-02  16.410000  17.000000  16.250000  12.683227   40980600  CSCO    4"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uqC6c40Zh1iH"
      },
      "source": [
        "# Part 4: Preprocess Data\n",
        "Data preprocessing is a crucial step for training a high quality machine learning model. We need to check for missing data and do feature engineering in order to convert the data into a model-ready state.\n",
        "* Add technical indicators. In practical trading, various information needs to be taken into account, for example the historical stock prices, current holding shares, technical indicators, etc. In this article, we demonstrate two trend-following technical indicators: MACD and RSI.\n",
        "* Add turbulence index. Risk-aversion reflects whether an investor will choose to preserve the capital. It also influences one's trading strategy when facing different market volatility level. To control the risk in a worst-case scenario, such as financial crisis of 2007–2008, FinRL employs the financial turbulence index that measures extreme asset price fluctuation."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        },
        "id": "PmKP-1ii3RLS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f04f7c44-c6ad-48db-9b9c-dbc72ce47f80"
      },
      "source": [
        "fe = FeatureEngineer(\n",
        "                    use_technical_indicator=True,\n",
        "                    tech_indicator_list = config.TECHNICAL_INDICATORS_LIST,\n",
        "                    use_turbulence=True,\n",
        "                    user_defined_feature = False)\n",
        "\n",
        "processed = fe.preprocess_data(df)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Successfully added technical indicators\n",
            "Successfully added turbulence index\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kixon2tR3RLT"
      },
      "source": [
        "list_ticker = processed[\"tic\"].unique().tolist()\n",
        "list_date = list(pd.date_range(processed['date'].min(),processed['date'].max()).astype(str))\n",
        "combination = list(itertools.product(list_date,list_ticker))\n",
        "\n",
        "processed_full = pd.DataFrame(combination,columns=[\"date\",\"tic\"]).merge(processed,on=[\"date\",\"tic\"],how=\"left\")\n",
        "processed_full = processed_full[processed_full['date'].isin(processed['date'])]\n",
        "processed_full = processed_full.sort_values(['date','tic'])\n",
        "\n",
        "processed_full = processed_full.fillna(0)"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 549
        },
        "id": "grvhGJJII3Xn",
        "outputId": "0523691f-99d7-4f11-d72f-cf20622f5f28"
      },
      "source": [
        "processed_full.sort_values(['date','tic'],ignore_index=True).head(10)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>tic</th>\n",
              "      <th>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>close</th>\n",
              "      <th>volume</th>\n",
              "      <th>day</th>\n",
              "      <th>macd</th>\n",
              "      <th>boll_ub</th>\n",
              "      <th>boll_lb</th>\n",
              "      <th>rsi_30</th>\n",
              "      <th>cci_30</th>\n",
              "      <th>dx_30</th>\n",
              "      <th>close_30_sma</th>\n",
              "      <th>close_60_sma</th>\n",
              "      <th>turbulence</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>3.067143</td>\n",
              "      <td>3.251429</td>\n",
              "      <td>3.041429</td>\n",
              "      <td>2.791740</td>\n",
              "      <td>746015200.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.017278</td>\n",
              "      <td>2.684025</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>66.666667</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>2.791740</td>\n",
              "      <td>2.791740</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>AXP</td>\n",
              "      <td>18.570000</td>\n",
              "      <td>19.520000</td>\n",
              "      <td>18.400000</td>\n",
              "      <td>15.745411</td>\n",
              "      <td>10955700.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.002643</td>\n",
              "      <td>3.017278</td>\n",
              "      <td>2.684025</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>66.666667</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>2.850651</td>\n",
              "      <td>2.850651</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>BA</td>\n",
              "      <td>42.799999</td>\n",
              "      <td>45.560001</td>\n",
              "      <td>42.779999</td>\n",
              "      <td>33.941101</td>\n",
              "      <td>7010200.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.001880</td>\n",
              "      <td>2.972787</td>\n",
              "      <td>2.735796</td>\n",
              "      <td>70.355711</td>\n",
              "      <td>46.771878</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>2.854292</td>\n",
              "      <td>2.854292</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>CAT</td>\n",
              "      <td>44.910000</td>\n",
              "      <td>46.980000</td>\n",
              "      <td>44.709999</td>\n",
              "      <td>32.978760</td>\n",
              "      <td>7117200.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>-0.000746</td>\n",
              "      <td>2.951725</td>\n",
              "      <td>2.729582</td>\n",
              "      <td>50.429389</td>\n",
              "      <td>-29.777993</td>\n",
              "      <td>43.607834</td>\n",
              "      <td>2.840654</td>\n",
              "      <td>2.840654</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>CSCO</td>\n",
              "      <td>16.410000</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>16.250000</td>\n",
              "      <td>12.683227</td>\n",
              "      <td>40980600.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>-0.000088</td>\n",
              "      <td>2.939568</td>\n",
              "      <td>2.746169</td>\n",
              "      <td>60.227126</td>\n",
              "      <td>-9.019317</td>\n",
              "      <td>48.357918</td>\n",
              "      <td>2.842869</td>\n",
              "      <td>2.842869</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>CVX</td>\n",
              "      <td>74.230003</td>\n",
              "      <td>77.300003</td>\n",
              "      <td>73.580002</td>\n",
              "      <td>47.373642</td>\n",
              "      <td>13695900.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>-0.002480</td>\n",
              "      <td>2.931445</td>\n",
              "      <td>2.735506</td>\n",
              "      <td>47.932736</td>\n",
              "      <td>-27.237701</td>\n",
              "      <td>49.952380</td>\n",
              "      <td>2.833476</td>\n",
              "      <td>2.833476</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>DD</td>\n",
              "      <td>21.605234</td>\n",
              "      <td>22.060680</td>\n",
              "      <td>20.993229</td>\n",
              "      <td>15.402899</td>\n",
              "      <td>13251037.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>-0.006646</td>\n",
              "      <td>2.938422</td>\n",
              "      <td>2.698235</td>\n",
              "      <td>40.237370</td>\n",
              "      <td>-101.614424</td>\n",
              "      <td>9.320766</td>\n",
              "      <td>2.818329</td>\n",
              "      <td>2.818329</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>DIS</td>\n",
              "      <td>22.760000</td>\n",
              "      <td>24.030001</td>\n",
              "      <td>22.500000</td>\n",
              "      <td>20.597496</td>\n",
              "      <td>9796600.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>-0.010724</td>\n",
              "      <td>2.943226</td>\n",
              "      <td>2.663404</td>\n",
              "      <td>37.181826</td>\n",
              "      <td>-113.576183</td>\n",
              "      <td>3.614243</td>\n",
              "      <td>2.803315</td>\n",
              "      <td>2.803315</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>GS</td>\n",
              "      <td>84.019997</td>\n",
              "      <td>87.620003</td>\n",
              "      <td>82.190002</td>\n",
              "      <td>72.844467</td>\n",
              "      <td>14088500.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>-0.017016</td>\n",
              "      <td>2.960305</td>\n",
              "      <td>2.606701</td>\n",
              "      <td>31.067739</td>\n",
              "      <td>-136.004971</td>\n",
              "      <td>17.746098</td>\n",
              "      <td>2.783503</td>\n",
              "      <td>2.783503</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>HD</td>\n",
              "      <td>23.070000</td>\n",
              "      <td>24.190001</td>\n",
              "      <td>22.959999</td>\n",
              "      <td>17.909452</td>\n",
              "      <td>14902500.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>-0.024322</td>\n",
              "      <td>2.978173</td>\n",
              "      <td>2.545136</td>\n",
              "      <td>27.267345</td>\n",
              "      <td>-166.471239</td>\n",
              "      <td>43.235022</td>\n",
              "      <td>2.761654</td>\n",
              "      <td>2.761654</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         date   tic       open  ...  close_30_sma  close_60_sma  turbulence\n",
              "0  2009-01-02  AAPL   3.067143  ...      2.791740      2.791740         0.0\n",
              "1  2009-01-02   AXP  18.570000  ...      2.850651      2.850651         0.0\n",
              "2  2009-01-02    BA  42.799999  ...      2.854292      2.854292         0.0\n",
              "3  2009-01-02   CAT  44.910000  ...      2.840654      2.840654         0.0\n",
              "4  2009-01-02  CSCO  16.410000  ...      2.842869      2.842869         0.0\n",
              "5  2009-01-02   CVX  74.230003  ...      2.833476      2.833476         0.0\n",
              "6  2009-01-02    DD  21.605234  ...      2.818329      2.818329         0.0\n",
              "7  2009-01-02   DIS  22.760000  ...      2.803315      2.803315         0.0\n",
              "8  2009-01-02    GS  84.019997  ...      2.783503      2.783503         0.0\n",
              "9  2009-01-02    HD  23.070000  ...      2.761654      2.761654         0.0\n",
              "\n",
              "[10 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-QsYaY0Dh1iw"
      },
      "source": [
        "<a id='4'></a>\n",
        "# Part 5. Design Environment\n",
        "Considering the stochastic and interactive nature of the automated stock trading tasks, a financial task is modeled as a **Markov Decision Process (MDP)** problem. The training process involves observing stock price change, taking an action and reward's calculation to have the agent adjusting its strategy accordingly. By interacting with the environment, the trading agent will derive a trading strategy with the maximized rewards as time proceeds.\n",
        "\n",
        "Our trading environments, based on OpenAI Gym framework, simulate live stock markets with real market data according to the principle of time-driven simulation.\n",
        "\n",
        "The action space describes the allowed actions that the agent interacts with the environment. Normally, action a includes three actions: {-1, 0, 1}, where -1, 0, 1 represent selling, holding, and buying one share. Also, an action can be carried upon multiple shares. We use an action space {-k,…,-1, 0, 1, …, k}, where k denotes the number of shares to buy and -k denotes the number of shares to sell. For example, \"Buy 10 shares of AAPL\" or \"Sell 10 shares of AAPL\" are 10 or -10, respectively. The continuous action space needs to be normalized to [-1, 1], since the policy is defined on a Gaussian distribution, which needs to be normalized and symmetric."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5TOhcryx44bb"
      },
      "source": [
        "## Training data split: 2009-01-01 to 2018-12-31\n",
        "## Trade data split: 2019-01-01 to 2020-09-30"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W0qaVGjLtgbI",
        "outputId": "f7dd4f68-51b0-4497-867d-e6cb462f9474"
      },
      "source": [
        "train = data_split(processed_full, '2009-01-01','2019-01-01')\n",
        "trade = data_split(processed_full, '2019-01-01','2021-01-01')\n",
        "print(len(train))\n",
        "print(len(trade))"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "75480\n",
            "15150\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309
        },
        "id": "p52zNCOhTtLR",
        "outputId": "ef026856-f15e-49e3-d3d9-b45eaed9f3bb"
      },
      "source": [
        "train.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>tic</th>\n",
              "      <th>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>close</th>\n",
              "      <th>volume</th>\n",
              "      <th>day</th>\n",
              "      <th>macd</th>\n",
              "      <th>boll_ub</th>\n",
              "      <th>boll_lb</th>\n",
              "      <th>rsi_30</th>\n",
              "      <th>cci_30</th>\n",
              "      <th>dx_30</th>\n",
              "      <th>close_30_sma</th>\n",
              "      <th>close_60_sma</th>\n",
              "      <th>turbulence</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>3.067143</td>\n",
              "      <td>3.251429</td>\n",
              "      <td>3.041429</td>\n",
              "      <td>2.791740</td>\n",
              "      <td>746015200.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.017278</td>\n",
              "      <td>2.684025</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>66.666667</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>2.791740</td>\n",
              "      <td>2.791740</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>AXP</td>\n",
              "      <td>18.570000</td>\n",
              "      <td>19.520000</td>\n",
              "      <td>18.400000</td>\n",
              "      <td>15.745411</td>\n",
              "      <td>10955700.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.002643</td>\n",
              "      <td>3.017278</td>\n",
              "      <td>2.684025</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>66.666667</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>2.850651</td>\n",
              "      <td>2.850651</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>BA</td>\n",
              "      <td>42.799999</td>\n",
              "      <td>45.560001</td>\n",
              "      <td>42.779999</td>\n",
              "      <td>33.941101</td>\n",
              "      <td>7010200.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.001880</td>\n",
              "      <td>2.972787</td>\n",
              "      <td>2.735796</td>\n",
              "      <td>70.355711</td>\n",
              "      <td>46.771878</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>2.854292</td>\n",
              "      <td>2.854292</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>CAT</td>\n",
              "      <td>44.910000</td>\n",
              "      <td>46.980000</td>\n",
              "      <td>44.709999</td>\n",
              "      <td>32.978760</td>\n",
              "      <td>7117200.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>-0.000746</td>\n",
              "      <td>2.951725</td>\n",
              "      <td>2.729582</td>\n",
              "      <td>50.429389</td>\n",
              "      <td>-29.777993</td>\n",
              "      <td>43.607834</td>\n",
              "      <td>2.840654</td>\n",
              "      <td>2.840654</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>CSCO</td>\n",
              "      <td>16.410000</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>16.250000</td>\n",
              "      <td>12.683227</td>\n",
              "      <td>40980600.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>-0.000088</td>\n",
              "      <td>2.939568</td>\n",
              "      <td>2.746169</td>\n",
              "      <td>60.227126</td>\n",
              "      <td>-9.019317</td>\n",
              "      <td>48.357918</td>\n",
              "      <td>2.842869</td>\n",
              "      <td>2.842869</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         date   tic       open  ...  close_30_sma  close_60_sma  turbulence\n",
              "0  2009-01-02  AAPL   3.067143  ...      2.791740      2.791740         0.0\n",
              "0  2009-01-02   AXP  18.570000  ...      2.850651      2.850651         0.0\n",
              "0  2009-01-02    BA  42.799999  ...      2.854292      2.854292         0.0\n",
              "0  2009-01-02   CAT  44.910000  ...      2.840654      2.840654         0.0\n",
              "0  2009-01-02  CSCO  16.410000  ...      2.842869      2.842869         0.0\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309
        },
        "id": "k9zU9YaTTvFq",
        "outputId": "a449c5b4-b244-44b8-b756-10c82fe9546b"
      },
      "source": [
        "trade.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>tic</th>\n",
              "      <th>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>close</th>\n",
              "      <th>volume</th>\n",
              "      <th>day</th>\n",
              "      <th>macd</th>\n",
              "      <th>boll_ub</th>\n",
              "      <th>boll_lb</th>\n",
              "      <th>rsi_30</th>\n",
              "      <th>cci_30</th>\n",
              "      <th>dx_30</th>\n",
              "      <th>close_30_sma</th>\n",
              "      <th>close_60_sma</th>\n",
              "      <th>turbulence</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2019-01-02</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>38.722500</td>\n",
              "      <td>39.712502</td>\n",
              "      <td>38.557499</td>\n",
              "      <td>38.505024</td>\n",
              "      <td>148158800.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.335191</td>\n",
              "      <td>334.139821</td>\n",
              "      <td>308.258949</td>\n",
              "      <td>47.826590</td>\n",
              "      <td>-27.772146</td>\n",
              "      <td>14.525416</td>\n",
              "      <td>314.202890</td>\n",
              "      <td>312.704339</td>\n",
              "      <td>51.409032</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2019-01-02</td>\n",
              "      <td>AXP</td>\n",
              "      <td>93.910004</td>\n",
              "      <td>96.269997</td>\n",
              "      <td>93.769997</td>\n",
              "      <td>92.319603</td>\n",
              "      <td>4175400.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.775741</td>\n",
              "      <td>335.611436</td>\n",
              "      <td>305.851699</td>\n",
              "      <td>46.156688</td>\n",
              "      <td>-67.860390</td>\n",
              "      <td>22.313462</td>\n",
              "      <td>314.159711</td>\n",
              "      <td>312.579659</td>\n",
              "      <td>51.409032</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2019-01-02</td>\n",
              "      <td>BA</td>\n",
              "      <td>316.190002</td>\n",
              "      <td>323.950012</td>\n",
              "      <td>313.709991</td>\n",
              "      <td>314.645142</td>\n",
              "      <td>3292200.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>-0.338838</td>\n",
              "      <td>336.485023</td>\n",
              "      <td>304.296566</td>\n",
              "      <td>46.806048</td>\n",
              "      <td>-72.067253</td>\n",
              "      <td>24.597730</td>\n",
              "      <td>314.064055</td>\n",
              "      <td>312.452928</td>\n",
              "      <td>51.409032</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2019-01-02</td>\n",
              "      <td>CAT</td>\n",
              "      <td>124.029999</td>\n",
              "      <td>127.879997</td>\n",
              "      <td>123.000000</td>\n",
              "      <td>118.671188</td>\n",
              "      <td>4783200.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>-0.655001</td>\n",
              "      <td>336.668731</td>\n",
              "      <td>303.510025</td>\n",
              "      <td>49.792068</td>\n",
              "      <td>-33.729612</td>\n",
              "      <td>14.054906</td>\n",
              "      <td>314.492846</td>\n",
              "      <td>312.392662</td>\n",
              "      <td>51.409032</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2019-01-02</td>\n",
              "      <td>CSCO</td>\n",
              "      <td>42.279999</td>\n",
              "      <td>43.200001</td>\n",
              "      <td>42.209999</td>\n",
              "      <td>40.057236</td>\n",
              "      <td>23833500.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>-0.159995</td>\n",
              "      <td>336.703986</td>\n",
              "      <td>304.162298</td>\n",
              "      <td>53.389344</td>\n",
              "      <td>31.217425</td>\n",
              "      <td>7.135987</td>\n",
              "      <td>315.392609</td>\n",
              "      <td>312.451774</td>\n",
              "      <td>51.409032</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         date   tic        open  ...  close_30_sma  close_60_sma  turbulence\n",
              "0  2019-01-02  AAPL   38.722500  ...    314.202890    312.704339   51.409032\n",
              "0  2019-01-02   AXP   93.910004  ...    314.159711    312.579659   51.409032\n",
              "0  2019-01-02    BA  316.190002  ...    314.064055    312.452928   51.409032\n",
              "0  2019-01-02   CAT  124.029999  ...    314.492846    312.392662   51.409032\n",
              "0  2019-01-02  CSCO   42.279999  ...    315.392609    312.451774   51.409032\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zYN573SOHhxG",
        "outputId": "0c82387b-5820-4825-8a66-9d67f024f88e"
      },
      "source": [
        "config.TECHNICAL_INDICATORS_LIST"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['macd',\n",
              " 'boll_ub',\n",
              " 'boll_lb',\n",
              " 'rsi_30',\n",
              " 'cci_30',\n",
              " 'dx_30',\n",
              " 'close_30_sma',\n",
              " 'close_60_sma']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q2zqII8rMIqn",
        "outputId": "de67ad03-fec4-4d1b-fcc5-2873406dde70"
      },
      "source": [
        "stock_dimension = len(train.tic.unique())\n",
        "state_space = 1 + 2*stock_dimension + len(config.TECHNICAL_INDICATORS_LIST)*stock_dimension\n",
        "print(f\"Stock Dimension: {stock_dimension}, State Space: {state_space}\")\n"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Stock Dimension: 30, State Space: 301\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AWyp84Ltto19"
      },
      "source": [
        "env_kwargs = {\n",
        "    \"hmax\": 100, \n",
        "    \"initial_amount\": 1000000, \n",
        "    \"buy_cost_pct\": 0.001,\n",
        "    \"sell_cost_pct\": 0.001,\n",
        "    \"state_space\": state_space, \n",
        "    \"stock_dim\": stock_dimension, \n",
        "    \"tech_indicator_list\": config.TECHNICAL_INDICATORS_LIST, \n",
        "    \"action_space\": stock_dimension, \n",
        "    \"reward_scaling\": 1e-4\n",
        "    \n",
        "}\n",
        "\n",
        "e_train_gym = StockTradingEnv(df = train, **env_kwargs)"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "64EoqOrQjiVf"
      },
      "source": [
        "## Environment for Training\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xwSvvPjutpqS",
        "outputId": "259bc17b-f03a-498e-87f5-ab3406413096"
      },
      "source": [
        "env_train, _ = e_train_gym.get_sb_env()\n",
        "print(type(env_train))"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'stable_baselines3.common.vec_env.dummy_vec_env.DummyVecEnv'>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HMNR5nHjh1iz"
      },
      "source": [
        "<a id='5'></a>\n",
        "# Part 6: Implement DRL Algorithms\n",
        "* The implementation of the DRL algorithms are based on **OpenAI Baselines** and **Stable Baselines**. Stable Baselines is a fork of OpenAI Baselines, with a major structural refactoring, and code cleanups.\n",
        "* FinRL library includes fine-tuned standard DRL algorithms, such as DQN, DDPG,\n",
        "Multi-Agent DDPG, PPO, SAC, A2C and TD3. We also allow users to\n",
        "design their own DRL algorithms by adapting these DRL algorithms."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "364PsqckttcQ"
      },
      "source": [
        "agent = DRLAgent(env = env_train)"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YDmqOyF9h1iz"
      },
      "source": [
        "### Model Training: 5 models, A2C DDPG, PPO, TD3, SAC\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uijiWgkuh1jB"
      },
      "source": [
        "### Model 1: A2C\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GUCnkn-HIbmj",
        "outputId": "2fdb297a-8d35-4c7e-806f-de859d70e19e"
      },
      "source": [
        "agent = DRLAgent(env = env_train)\n",
        "model_a2c = agent.get_model(\"a2c\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'n_steps': 5, 'ent_coef': 0.01, 'learning_rate': 0.0007}\n",
            "Using cuda device\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/opt/conda/lib/python3.6/site-packages/torch/cuda/__init__.py:81: UserWarning: \n",
            "    Found GPU1 GeForce GTX 760 which is of cuda capability 3.0.\n",
            "    PyTorch no longer supports this GPU because it is too old.\n",
            "    The minimum cuda capability that we support is 3.5.\n",
            "    \n",
            "  warnings.warn(old_gpu_warn % (d, name, major, capability[1]))\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0GVpkWGqH4-D",
        "outputId": "9eb09ba2-fc4b-46a1-ea3d-bd9b3bfefffd"
      },
      "source": [
        "trained_a2c = agent.train_model(model=model_a2c, \n",
        "                             tb_log_name='a2c',\n",
        "                             total_timesteps=100000)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Logging to tensorboard_log/a2c/a2c_4\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 81       |\n",
            "|    iterations         | 100      |\n",
            "|    time_elapsed       | 6        |\n",
            "|    total_timesteps    | 500      |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.6    |\n",
            "|    explained_variance | 0.338    |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 99       |\n",
            "|    policy_loss        | -41.5    |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 1.22     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 84       |\n",
            "|    iterations         | 200      |\n",
            "|    time_elapsed       | 11       |\n",
            "|    total_timesteps    | 1000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.6    |\n",
            "|    explained_variance | 0.0336   |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 199      |\n",
            "|    policy_loss        | 10.6     |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 1.05     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 86       |\n",
            "|    iterations         | 300      |\n",
            "|    time_elapsed       | 17       |\n",
            "|    total_timesteps    | 1500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.6    |\n",
            "|    explained_variance | 0.0456   |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 299      |\n",
            "|    policy_loss        | -416     |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 106      |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 86       |\n",
            "|    iterations         | 400      |\n",
            "|    time_elapsed       | 23       |\n",
            "|    total_timesteps    | 2000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.7    |\n",
            "|    explained_variance | -0.0152  |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 399      |\n",
            "|    policy_loss        | -19.6    |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 1.25     |\n",
            "------------------------------------\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-22-e000b3012843>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m trained_a2c = agent.train_model(model=model_a2c, \n\u001b[1;32m      2\u001b[0m                              \u001b[0mtb_log_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'a2c'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m                              total_timesteps=100000)\n\u001b[0m",
            "\u001b[0;32m/home/finrl/model/models.py\u001b[0m in \u001b[0;36mtrain_model\u001b[0;34m(self, model, tb_log_name, total_timesteps)\u001b[0m\n\u001b[1;32m    120\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    121\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mtrain_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtb_log_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtotal_timesteps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 122\u001b[0;31m         \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlearn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtotal_timesteps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtotal_timesteps\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtb_log_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtb_log_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    123\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    124\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/stable_baselines3/a2c/a2c.py\u001b[0m in \u001b[0;36mlearn\u001b[0;34m(self, total_timesteps, callback, log_interval, eval_env, eval_freq, n_eval_episodes, tb_log_name, eval_log_path, reset_num_timesteps)\u001b[0m\n\u001b[1;32m    197\u001b[0m             \u001b[0mtb_log_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtb_log_name\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    198\u001b[0m             \u001b[0meval_log_path\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0meval_log_path\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 199\u001b[0;31m             \u001b[0mreset_num_timesteps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mreset_num_timesteps\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    200\u001b[0m         )\n",
            "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/stable_baselines3/common/on_policy_algorithm.py\u001b[0m in \u001b[0;36mlearn\u001b[0;34m(self, total_timesteps, callback, log_interval, eval_env, eval_freq, n_eval_episodes, tb_log_name, eval_log_path, reset_num_timesteps)\u001b[0m\n\u001b[1;32m    225\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_timesteps\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mtotal_timesteps\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    226\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 227\u001b[0;31m             \u001b[0mcontinue_training\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcollect_rollouts\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrollout_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_rollout_steps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_steps\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    228\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    229\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mcontinue_training\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/stable_baselines3/common/on_policy_algorithm.py\u001b[0m in \u001b[0;36mcollect_rollouts\u001b[0;34m(self, env, callback, rollout_buffer, n_rollout_steps)\u001b[0m\n\u001b[1;32m    166\u001b[0m                 \u001b[0mclipped_actions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mactions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maction_space\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlow\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maction_space\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhigh\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    167\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 168\u001b[0;31m             \u001b[0mnew_obs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrewards\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdones\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minfos\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0menv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclipped_actions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    169\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    170\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_timesteps\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0menv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_envs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/stable_baselines3/common/vec_env/base_vec_env.py\u001b[0m in \u001b[0;36mstep\u001b[0;34m(self, actions)\u001b[0m\n\u001b[1;32m    161\u001b[0m         \"\"\"\n\u001b[1;32m    162\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mactions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 163\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep_wait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    164\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    165\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_images\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mSequence\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/stable_baselines3/common/vec_env/dummy_vec_env.py\u001b[0m in \u001b[0;36mstep_wait\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     42\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0menv_idx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_envs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     43\u001b[0m             obs, self.buf_rews[env_idx], self.buf_dones[env_idx], self.buf_infos[env_idx] = self.envs[env_idx].step(\n\u001b[0;32m---> 44\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mactions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0menv_idx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     45\u001b[0m             )\n\u001b[1;32m     46\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuf_dones\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0menv_idx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/home/finrl/env/env_stocktrading.py\u001b[0m in \u001b[0;36mstep\u001b[0;34m(self, actions)\u001b[0m\n\u001b[1;32m    248\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mturbulence_threshold\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    249\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mturbulence\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'turbulence'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 250\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstate\u001b[0m \u001b[0;34m=\u001b[0m  \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_update_state\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    251\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    252\u001b[0m             \u001b[0mend_total_asset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/home/finrl/env/env_stocktrading.py\u001b[0m in \u001b[0;36m_update_state\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    321\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    322\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_update_state\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 323\u001b[0;31m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtic\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m>\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    324\u001b[0m             \u001b[0;31m# for multiple stock\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    325\u001b[0m             \u001b[0mstate\u001b[0m \u001b[0;34m=\u001b[0m  \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36munique\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1879\u001b[0m         \u001b[0mCategories\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'a'\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;34m'b'\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;34m'c'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1880\u001b[0m         \"\"\"\n\u001b[0;32m-> 1881\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1882\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1883\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/base.py\u001b[0m in \u001b[0;36munique\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1263\u001b[0m                     \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1264\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1265\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0munique1d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1266\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1267\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/algorithms.py\u001b[0m in \u001b[0;36munique\u001b[0;34m(values)\u001b[0m\n\u001b[1;32m    397\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    398\u001b[0m     \u001b[0mtable\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhtable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 399\u001b[0;31m     \u001b[0muniques\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtable\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    400\u001b[0m     \u001b[0muniques\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_reconstruct_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0muniques\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moriginal\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moriginal\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    401\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0muniques\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MRiOtrywfAo1"
      },
      "source": [
        "### Model 2: DDPG"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M2YadjfnLwgt",
        "outputId": "3b2a8f89-0561-4083-a015-fbee11693037"
      },
      "source": [
        "agent = DRLAgent(env = env_train)\n",
        "model_ddpg = agent.get_model(\"ddpg\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'batch_size': 128, 'buffer_size': 50000, 'learning_rate': 0.001}\n",
            "Using cpu device\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "tCDa78rqfO_a",
        "jupyter": {
          "outputs_hidden": true
        },
        "outputId": "f651f8be-4c93-4b1e-c88a-7e3a09976693"
      },
      "source": [
        "trained_ddpg = agent.train_model(model=model_ddpg, \n",
        "                             tb_log_name='ddpg',\n",
        "                             total_timesteps=50000)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Logging to tensorboard_log/ddpg/ddpg_1\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:3761309.8057632465\n",
            "total_reward:2761309.8057632465\n",
            "total_cost:  6807.077776350557\n",
            "total_trades:  39070\n",
            "Sharpe:  1.0173492167488003\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4423657.61673363\n",
            "total_reward:3423657.61673363\n",
            "total_cost:  1277.392035166502\n",
            "total_trades:  32819\n",
            "Sharpe:  0.8726982452731067\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4423657.61673363\n",
            "total_reward:3423657.61673363\n",
            "total_cost:  1277.392035166502\n",
            "total_trades:  32819\n",
            "Sharpe:  0.8726982452731067\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4423657.61673363\n",
            "total_reward:3423657.61673363\n",
            "total_cost:  1277.392035166502\n",
            "total_trades:  32819\n",
            "Sharpe:  0.8726982452731067\n",
            "=================================\n",
            "---------------------------------\n",
            "| time/              |          |\n",
            "|    episodes        | 4        |\n",
            "|    fps             | 38       |\n",
            "|    time_elapsed    | 258      |\n",
            "|    total timesteps | 10064    |\n",
            "| train/             |          |\n",
            "|    actor_loss      | -2.81    |\n",
            "|    critic_loss     | 272      |\n",
            "|    learning_rate   | 0.001    |\n",
            "|    n_updates       | 7548     |\n",
            "---------------------------------\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4423657.61673363\n",
            "total_reward:3423657.61673363\n",
            "total_cost:  1277.392035166502\n",
            "total_trades:  32819\n",
            "Sharpe:  0.8726982452731067\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4423657.61673363\n",
            "total_reward:3423657.61673363\n",
            "total_cost:  1277.392035166502\n",
            "total_trades:  32819\n",
            "Sharpe:  0.8726982452731067\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4423657.61673363\n",
            "total_reward:3423657.61673363\n",
            "total_cost:  1277.392035166502\n",
            "total_trades:  32819\n",
            "Sharpe:  0.8726982452731067\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4423657.61673363\n",
            "total_reward:3423657.61673363\n",
            "total_cost:  1277.392035166502\n",
            "total_trades:  32819\n",
            "Sharpe:  0.8726982452731067\n",
            "=================================\n",
            "---------------------------------\n",
            "| time/              |          |\n",
            "|    episodes        | 8        |\n",
            "|    fps             | 33       |\n",
            "|    time_elapsed    | 604      |\n",
            "|    total timesteps | 20128    |\n",
            "| train/             |          |\n",
            "|    actor_loss      | -8.32    |\n",
            "|    critic_loss     | 12.8     |\n",
            "|    learning_rate   | 0.001    |\n",
            "|    n_updates       | 17612    |\n",
            "---------------------------------\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4423657.61673363\n",
            "total_reward:3423657.61673363\n",
            "total_cost:  1277.392035166502\n",
            "total_trades:  32819\n",
            "Sharpe:  0.8726982452731067\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4423657.61673363\n",
            "total_reward:3423657.61673363\n",
            "total_cost:  1277.392035166502\n",
            "total_trades:  32819\n",
            "Sharpe:  0.8726982452731067\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4423657.61673363\n",
            "total_reward:3423657.61673363\n",
            "total_cost:  1277.392035166502\n",
            "total_trades:  32819\n",
            "Sharpe:  0.8726982452731067\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4423657.61673363\n",
            "total_reward:3423657.61673363\n",
            "total_cost:  1277.392035166502\n",
            "total_trades:  32819\n",
            "Sharpe:  0.8726982452731067\n",
            "=================================\n",
            "---------------------------------\n",
            "| time/              |          |\n",
            "|    episodes        | 12       |\n",
            "|    fps             | 31       |\n",
            "|    time_elapsed    | 953      |\n",
            "|    total timesteps | 30192    |\n",
            "| train/             |          |\n",
            "|    actor_loss      | -9.46    |\n",
            "|    critic_loss     | 4.31     |\n",
            "|    learning_rate   | 0.001    |\n",
            "|    n_updates       | 27676    |\n",
            "---------------------------------\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4423657.61673363\n",
            "total_reward:3423657.61673363\n",
            "total_cost:  1277.392035166502\n",
            "total_trades:  32819\n",
            "Sharpe:  0.8726982452731067\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4423657.61673363\n",
            "total_reward:3423657.61673363\n",
            "total_cost:  1277.392035166502\n",
            "total_trades:  32819\n",
            "Sharpe:  0.8726982452731067\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4423657.61673363\n",
            "total_reward:3423657.61673363\n",
            "total_cost:  1277.392035166502\n",
            "total_trades:  32819\n",
            "Sharpe:  0.8726982452731067\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4423657.61673363\n",
            "total_reward:3423657.61673363\n",
            "total_cost:  1277.392035166502\n",
            "total_trades:  32819\n",
            "Sharpe:  0.8726982452731067\n",
            "=================================\n",
            "---------------------------------\n",
            "| time/              |          |\n",
            "|    episodes        | 16       |\n",
            "|    fps             | 30       |\n",
            "|    time_elapsed    | 1309     |\n",
            "|    total timesteps | 40256    |\n",
            "| train/             |          |\n",
            "|    actor_loss      | -10.2    |\n",
            "|    critic_loss     | 3.19     |\n",
            "|    learning_rate   | 0.001    |\n",
            "|    n_updates       | 37740    |\n",
            "---------------------------------\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4423657.61673363\n",
            "total_reward:3423657.61673363\n",
            "total_cost:  1277.392035166502\n",
            "total_trades:  32819\n",
            "Sharpe:  0.8726982452731067\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4423657.61673363\n",
            "total_reward:3423657.61673363\n",
            "total_cost:  1277.392035166502\n",
            "total_trades:  32819\n",
            "Sharpe:  0.8726982452731067\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4423657.61673363\n",
            "total_reward:3423657.61673363\n",
            "total_cost:  1277.392035166502\n",
            "total_trades:  32819\n",
            "Sharpe:  0.8726982452731067\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4423657.61673363\n",
            "total_reward:3423657.61673363\n",
            "total_cost:  1277.392035166502\n",
            "total_trades:  32819\n",
            "Sharpe:  0.8726982452731067\n",
            "=================================\n",
            "---------------------------------\n",
            "| time/              |          |\n",
            "|    episodes        | 20       |\n",
            "|    fps             | 30       |\n",
            "|    time_elapsed    | 1675     |\n",
            "|    total timesteps | 50320    |\n",
            "| train/             |          |\n",
            "|    actor_loss      | -11.1    |\n",
            "|    critic_loss     | 2.24     |\n",
            "|    learning_rate   | 0.001    |\n",
            "|    n_updates       | 47804    |\n",
            "---------------------------------\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_gDkU-j-fCmZ"
      },
      "source": [
        "### Model 3: PPO"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y5D5PFUhMzSV",
        "outputId": "1bd9a8e4-b094-4649-b0af-d6ae6562221f"
      },
      "source": [
        "agent = DRLAgent(env = env_train)\n",
        "PPO_PARAMS = {\n",
        "    \"n_steps\": 2048,\n",
        "    \"ent_coef\": 0.01,\n",
        "    \"learning_rate\": 0.00025,\n",
        "    \"batch_size\": 128,\n",
        "}\n",
        "model_ppo = agent.get_model(\"ppo\",model_kwargs = PPO_PARAMS)"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'n_steps': 2048, 'ent_coef': 0.01, 'learning_rate': 0.00025, 'batch_size': 128}\n",
            "Using cpu device\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 477
        },
        "collapsed": true,
        "id": "Gt8eIQKYM4G3",
        "jupyter": {
          "outputs_hidden": true
        },
        "outputId": "f0cdbe05-8c27-4c20-bd5e-a34c7c5923b0"
      },
      "source": [
        "trained_ppo = agent.train_model(model=model_ppo, \n",
        "                             tb_log_name='ppo',\n",
        "                             total_timesteps=50000)"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Logging to tensorboard_log/ppo/ppo_2\n",
            "-----------------------------\n",
            "| time/              |      |\n",
            "|    fps             | 107  |\n",
            "|    iterations      | 1    |\n",
            "|    time_elapsed    | 19   |\n",
            "|    total_timesteps | 2048 |\n",
            "-----------------------------\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-58-c744e952963e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m trained_ppo = agent.train_model(model=model_ppo, \n\u001b[1;32m      2\u001b[0m                              \u001b[0mtb_log_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'ppo'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m                              total_timesteps=50000)\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/finrl/model/models.py\u001b[0m in \u001b[0;36mtrain_model\u001b[0;34m(self, model, tb_log_name, total_timesteps)\u001b[0m\n\u001b[1;32m    122\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    123\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mtrain_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtb_log_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtotal_timesteps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 124\u001b[0;31m         \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlearn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtotal_timesteps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtotal_timesteps\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtb_log_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtb_log_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    125\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    126\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/stable_baselines3/ppo/ppo.py\u001b[0m in \u001b[0;36mlearn\u001b[0;34m(self, total_timesteps, callback, log_interval, eval_env, eval_freq, n_eval_episodes, tb_log_name, eval_log_path, reset_num_timesteps)\u001b[0m\n\u001b[1;32m    262\u001b[0m             \u001b[0mtb_log_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtb_log_name\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    263\u001b[0m             \u001b[0meval_log_path\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0meval_log_path\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 264\u001b[0;31m             \u001b[0mreset_num_timesteps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mreset_num_timesteps\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    265\u001b[0m         )\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/stable_baselines3/common/on_policy_algorithm.py\u001b[0m in \u001b[0;36mlearn\u001b[0;34m(self, total_timesteps, callback, log_interval, eval_env, eval_freq, n_eval_episodes, tb_log_name, eval_log_path, reset_num_timesteps)\u001b[0m\n\u001b[1;32m    220\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_timesteps\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mtotal_timesteps\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    221\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 222\u001b[0;31m             \u001b[0mcontinue_training\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcollect_rollouts\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrollout_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_rollout_steps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_steps\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    223\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    224\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mcontinue_training\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/stable_baselines3/common/on_policy_algorithm.py\u001b[0m in \u001b[0;36mcollect_rollouts\u001b[0;34m(self, env, callback, rollout_buffer, n_rollout_steps)\u001b[0m\n\u001b[1;32m    161\u001b[0m                 \u001b[0mclipped_actions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mactions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maction_space\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlow\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maction_space\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhigh\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    162\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 163\u001b[0;31m             \u001b[0mnew_obs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrewards\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdones\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minfos\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0menv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclipped_actions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    164\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    165\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_timesteps\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0menv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_envs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/stable_baselines3/common/vec_env/base_vec_env.py\u001b[0m in \u001b[0;36mstep\u001b[0;34m(self, actions)\u001b[0m\n\u001b[1;32m    148\u001b[0m         \"\"\"\n\u001b[1;32m    149\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mactions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 150\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep_wait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    151\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_images\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mSequence\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/stable_baselines3/common/vec_env/dummy_vec_env.py\u001b[0m in \u001b[0;36mstep_wait\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     42\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0menv_idx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_envs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     43\u001b[0m             obs, self.buf_rews[env_idx], self.buf_dones[env_idx], self.buf_infos[env_idx] = self.envs[env_idx].step(\n\u001b[0;32m---> 44\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mactions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0menv_idx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     45\u001b[0m             )\n\u001b[1;32m     46\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuf_dones\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0menv_idx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/finrl/env/env_stocktrading.py\u001b[0m in \u001b[0;36mstep\u001b[0;34m(self, actions)\u001b[0m\n\u001b[1;32m    253\u001b[0m             \u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstock_dim\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstock_dim\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstock_dim\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    254\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masset_memory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mend_total_asset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 255\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdate_memory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_date\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    256\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreward\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mend_total_asset\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mbegin_total_asset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    257\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrewards_memory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreward\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/finrl/env/env_stocktrading.py\u001b[0m in \u001b[0;36m_get_date\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    338\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    339\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_get_date\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 340\u001b[0;31m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtic\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m>\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    341\u001b[0m             \u001b[0mdate\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdate\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    342\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/series.py\u001b[0m in \u001b[0;36munique\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1879\u001b[0m         \u001b[0mCategories\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'a'\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;34m'b'\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;34m'c'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1880\u001b[0m         \"\"\"\n\u001b[0;32m-> 1881\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1882\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1883\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/base.py\u001b[0m in \u001b[0;36munique\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1263\u001b[0m                     \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1264\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1265\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0munique1d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1266\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1267\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/algorithms.py\u001b[0m in \u001b[0;36munique\u001b[0;34m(values)\u001b[0m\n\u001b[1;32m    394\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    395\u001b[0m     \u001b[0moriginal\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 396\u001b[0;31m     \u001b[0mhtable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_get_hashtable_algo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    397\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    398\u001b[0m     \u001b[0mtable\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhtable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/algorithms.py\u001b[0m in \u001b[0;36m_get_hashtable_algo\u001b[0;34m(values)\u001b[0m\n\u001b[1;32m    258\u001b[0m     \u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_ensure_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    259\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 260\u001b[0;31m     \u001b[0mndtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_check_object_for_strings\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    261\u001b[0m     \u001b[0mhtable\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_hashtables\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mndtype\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    262\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mhtable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/algorithms.py\u001b[0m in \u001b[0;36m_check_object_for_strings\u001b[0;34m(values)\u001b[0m\n\u001b[1;32m    299\u001b[0m         \u001b[0;31m# including nulls because that is the only difference between\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    300\u001b[0m         \u001b[0;31m# StringHashTable and ObjectHashtable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 301\u001b[0;31m         \u001b[0;32mif\u001b[0m \u001b[0mlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfer_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mskipna\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"string\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    302\u001b[0m             \u001b[0mndtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"string\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    303\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mndtype\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3Zpv4S0-fDBv"
      },
      "source": [
        "### Model 4: TD3"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JSAHhV4Xc-bh",
        "outputId": "e531db14-aab4-47d1-cc15-02c893ec66c9"
      },
      "source": [
        "agent = DRLAgent(env = env_train)\n",
        "TD3_PARAMS = {\"batch_size\": 100, \n",
        "              \"buffer_size\": 1000000, \n",
        "              \"learning_rate\": 0.001}\n",
        "\n",
        "model_td3 = agent.get_model(\"td3\",model_kwargs = TD3_PARAMS)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'batch_size': 100, 'buffer_size': 1000000, 'learning_rate': 0.001}\n",
            "Using cpu device\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OSRxNYAxdKpU",
        "outputId": "ddc4193c-884b-4a2c-9e49-31397e2cfbec"
      },
      "source": [
        "trained_td3 = agent.train_model(model=model_td3, \n",
        "                             tb_log_name='td3',\n",
        "                             total_timesteps=30000)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Logging to tensorboard_log/td3/td3_2\n",
            "---------------------------------\n",
            "| time/              |          |\n",
            "|    episodes        | 4        |\n",
            "|    fps             | 33       |\n",
            "|    time_elapsed    | 296      |\n",
            "|    total timesteps | 10064    |\n",
            "| train/             |          |\n",
            "|    actor_loss      | 67.9     |\n",
            "|    critic_loss     | 979      |\n",
            "|    learning_rate   | 0.001    |\n",
            "|    n_updates       | 7548     |\n",
            "---------------------------------\n",
            "day: 2515, episode: 10\n",
            "begin_total_asset:1000000.00\n",
            "end_total_asset:4438572.29\n",
            "total_reward:3438572.29\n",
            "total_cost: 1038.05\n",
            "total_trades: 40290\n",
            "Sharpe: 1.049\n",
            "=================================\n",
            "---------------------------------\n",
            "| time/              |          |\n",
            "|    episodes        | 8        |\n",
            "|    fps             | 30       |\n",
            "|    time_elapsed    | 669      |\n",
            "|    total timesteps | 20128    |\n",
            "| train/             |          |\n",
            "|    actor_loss      | 54       |\n",
            "|    critic_loss     | 199      |\n",
            "|    learning_rate   | 0.001    |\n",
            "|    n_updates       | 17612    |\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "| time/              |          |\n",
            "|    episodes        | 12       |\n",
            "|    fps             | 28       |\n",
            "|    time_elapsed    | 1052     |\n",
            "|    total timesteps | 30192    |\n",
            "| train/             |          |\n",
            "|    actor_loss      | 41.4     |\n",
            "|    critic_loss     | 25.2     |\n",
            "|    learning_rate   | 0.001    |\n",
            "|    n_updates       | 27676    |\n",
            "---------------------------------\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dr49PotrfG01"
      },
      "source": [
        "### Model 5: SAC"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xwOhVjqRkCdM",
        "outputId": "44fcd5ee-7ce3-4c21-b2c8-131b7bba8127"
      },
      "source": [
        "agent = DRLAgent(env = env_train)\n",
        "SAC_PARAMS = {\n",
        "    \"batch_size\": 128,\n",
        "    \"buffer_size\": 1000000,\n",
        "    \"learning_rate\": 0.0001,\n",
        "    \"learning_starts\": 100,\n",
        "    \"ent_coef\": \"auto_0.1\",\n",
        "}\n",
        "\n",
        "model_sac = agent.get_model(\"sac\",model_kwargs = SAC_PARAMS)"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'batch_size': 128, 'buffer_size': 1000000, 'learning_rate': 0.0001, 'learning_starts': 100, 'ent_coef': 'auto_0.1'}\n",
            "Using cpu device\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K8RSdKCckJyH",
        "outputId": "89e2ae3c-2559-4f93-82d4-168f9853db9e"
      },
      "source": [
        "trained_sac = agent.train_model(model=model_sac, \n",
        "                             tb_log_name='sac',\n",
        "                             total_timesteps=80000)"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Logging to tensorboard_log/sac/sac_1\n",
            "day: 2515, episode: 10\n",
            "begin_total_asset: 1000000.00\n",
            "end_total_asset: 3458901.03\n",
            "total_reward: 2458901.03\n",
            "total_cost: 184848.78\n",
            "total_trades: 59497\n",
            "Sharpe: 0.795\n",
            "=================================\n",
            "----------------------------------\n",
            "| environment/        |          |\n",
            "|    portfolio_value  | 3.46e+06 |\n",
            "|    total_cost       | 1.85e+05 |\n",
            "|    total_reward     | 2.46e+06 |\n",
            "|    total_reward_pct | 246      |\n",
            "|    total_trades     | 59497    |\n",
            "| time/               |          |\n",
            "|    episodes         | 4        |\n",
            "|    fps              | 25       |\n",
            "|    time_elapsed     | 390      |\n",
            "|    total timesteps  | 10064    |\n",
            "| train/              |          |\n",
            "|    actor_loss       | 802      |\n",
            "|    critic_loss      | 561      |\n",
            "|    ent_coef         | 0.217    |\n",
            "|    ent_coef_loss    | -37.2    |\n",
            "|    learning_rate    | 0.0001   |\n",
            "|    n_updates        | 9963     |\n",
            "----------------------------------\n",
            "----------------------------------\n",
            "| environment/        |          |\n",
            "|    portfolio_value  | 4.59e+06 |\n",
            "|    total_cost       | 9.06e+04 |\n",
            "|    total_reward     | 3.59e+06 |\n",
            "|    total_reward_pct | 359      |\n",
            "|    total_trades     | 56336    |\n",
            "| time/               |          |\n",
            "|    episodes         | 8        |\n",
            "|    fps              | 25       |\n",
            "|    time_elapsed     | 789      |\n",
            "|    total timesteps  | 20128    |\n",
            "| train/              |          |\n",
            "|    actor_loss       | 347      |\n",
            "|    critic_loss      | 47.3     |\n",
            "|    ent_coef         | 0.0813   |\n",
            "|    ent_coef_loss    | -121     |\n",
            "|    learning_rate    | 0.0001   |\n",
            "|    n_updates        | 20027    |\n",
            "----------------------------------\n",
            "----------------------------------\n",
            "| environment/        |          |\n",
            "|    portfolio_value  | 4.21e+06 |\n",
            "|    total_cost       | 5.02e+04 |\n",
            "|    total_reward     | 3.21e+06 |\n",
            "|    total_reward_pct | 321      |\n",
            "|    total_trades     | 55078    |\n",
            "| time/               |          |\n",
            "|    episodes         | 12       |\n",
            "|    fps              | 25       |\n",
            "|    time_elapsed     | 1191     |\n",
            "|    total timesteps  | 30192    |\n",
            "| train/              |          |\n",
            "|    actor_loss       | 190      |\n",
            "|    critic_loss      | 61.6     |\n",
            "|    ent_coef         | 0.0298   |\n",
            "|    ent_coef_loss    | -162     |\n",
            "|    learning_rate    | 0.0001   |\n",
            "|    n_updates        | 30091    |\n",
            "----------------------------------\n",
            "day: 2515, episode: 20\n",
            "begin_total_asset: 1000000.00\n",
            "end_total_asset: 5938251.09\n",
            "total_reward: 4938251.09\n",
            "total_cost: 28977.47\n",
            "total_trades: 53990\n",
            "Sharpe: 1.047\n",
            "=================================\n",
            "----------------------------------\n",
            "| environment/        |          |\n",
            "|    portfolio_value  | 6.11e+06 |\n",
            "|    total_cost       | 1.35e+04 |\n",
            "|    total_reward     | 5.11e+06 |\n",
            "|    total_reward_pct | 511      |\n",
            "|    total_trades     | 52631    |\n",
            "| time/               |          |\n",
            "|    episodes         | 16       |\n",
            "|    fps              | 25       |\n",
            "|    time_elapsed     | 1595     |\n",
            "|    total timesteps  | 40256    |\n",
            "| train/              |          |\n",
            "|    actor_loss       | 105      |\n",
            "|    critic_loss      | 34.7     |\n",
            "|    ent_coef         | 0.011    |\n",
            "|    ent_coef_loss    | -185     |\n",
            "|    learning_rate    | 0.0001   |\n",
            "|    n_updates        | 40155    |\n",
            "----------------------------------\n",
            "----------------------------------\n",
            "| environment/        |          |\n",
            "|    portfolio_value  | 5.55e+06 |\n",
            "|    total_cost       | 5.24e+03 |\n",
            "|    total_reward     | 4.55e+06 |\n",
            "|    total_reward_pct | 455      |\n",
            "|    total_trades     | 51269    |\n",
            "| time/               |          |\n",
            "|    episodes         | 20       |\n",
            "|    fps              | 25       |\n",
            "|    time_elapsed     | 2002     |\n",
            "|    total timesteps  | 50320    |\n",
            "| train/              |          |\n",
            "|    actor_loss       | 60.1     |\n",
            "|    critic_loss      | 13.5     |\n",
            "|    ent_coef         | 0.00413  |\n",
            "|    ent_coef_loss    | -174     |\n",
            "|    learning_rate    | 0.0001   |\n",
            "|    n_updates        | 50219    |\n",
            "----------------------------------\n",
            "day: 2515, episode: 30\n",
            "begin_total_asset: 1000000.00\n",
            "end_total_asset: 5343010.26\n",
            "total_reward: 4343010.26\n",
            "total_cost: 3151.20\n",
            "total_trades: 49562\n",
            "Sharpe: 1.017\n",
            "=================================\n",
            "----------------------------------\n",
            "| environment/        |          |\n",
            "|    portfolio_value  | 5.34e+06 |\n",
            "|    total_cost       | 3.15e+03 |\n",
            "|    total_reward     | 4.34e+06 |\n",
            "|    total_reward_pct | 434      |\n",
            "|    total_trades     | 49562    |\n",
            "| time/               |          |\n",
            "|    episodes         | 24       |\n",
            "|    fps              | 25       |\n",
            "|    time_elapsed     | 2410     |\n",
            "|    total timesteps  | 60384    |\n",
            "| train/              |          |\n",
            "|    actor_loss       | 34.1     |\n",
            "|    critic_loss      | 8.66     |\n",
            "|    ent_coef         | 0.00161  |\n",
            "|    ent_coef_loss    | -103     |\n",
            "|    learning_rate    | 0.0001   |\n",
            "|    n_updates        | 60283    |\n",
            "----------------------------------\n",
            "----------------------------------\n",
            "| environment/        |          |\n",
            "|    portfolio_value  | 4.67e+06 |\n",
            "|    total_cost       | 2.3e+03  |\n",
            "|    total_reward     | 3.67e+06 |\n",
            "|    total_reward_pct | 367      |\n",
            "|    total_trades     | 46109    |\n",
            "| time/               |          |\n",
            "|    episodes         | 28       |\n",
            "|    fps              | 24       |\n",
            "|    time_elapsed     | 2824     |\n",
            "|    total timesteps  | 70448    |\n",
            "| train/              |          |\n",
            "|    actor_loss       | 23.3     |\n",
            "|    critic_loss      | 10.3     |\n",
            "|    ent_coef         | 0.000899 |\n",
            "|    ent_coef_loss    | 1.3      |\n",
            "|    learning_rate    | 0.0001   |\n",
            "|    n_updates        | 70347    |\n",
            "----------------------------------\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f2wZgkQXh1jE"
      },
      "source": [
        "## Trading\n",
        "Assume that we have $1,000,000 initial capital at 2019-01-01. We use the DDPG model to trade Dow jones 30 stocks."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bEv5KGC8h1jE"
      },
      "source": [
        "### Set turbulence threshold\n",
        "Set the turbulence threshold to be greater than the maximum of insample turbulence data, if current turbulence index is greater than the threshold, then we assume that the current market is volatile"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "efwBi84ch1jE"
      },
      "source": [
        "data_turbulence = processed_full[(processed_full.date<'2019-01-01') & (processed_full.date>='2009-01-01')]\n",
        "insample_turbulence = data_turbulence.drop_duplicates(subset=['date'])"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VHZMBpSqh1jG",
        "outputId": "6dd6e5d6-38d1-4f60-ae70-1168757eb1fc"
      },
      "source": [
        "insample_turbulence.turbulence.describe()"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    2516.000000\n",
              "mean       33.278197\n",
              "std        33.999888\n",
              "min         0.000000\n",
              "25%        15.233650\n",
              "50%        25.166725\n",
              "75%        39.289944\n",
              "max       332.850050\n",
              "Name: turbulence, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yuwDPkV9h1jL"
      },
      "source": [
        "turbulence_threshold = np.quantile(insample_turbulence.turbulence.values,1)"
      ],
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wwoz_7VSh1jO",
        "outputId": "a22d3e00-8a19-4b18-dd3e-9d072a0f6aca"
      },
      "source": [
        "turbulence_threshold"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "332.8500501393391"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U5mmgQF_h1jQ"
      },
      "source": [
        "### Trade\n",
        "\n",
        "DRL model needs to update periodically in order to take full advantage of the data, ideally we need to retrain our model yearly, quarterly, or monthly. We also need to tune the parameters along the way, in this notebook I only use the in-sample data from 2009-01 to 2018-12 to tune the parameters once, so there is some alpha decay here as the length of trade date extends. \n",
        "\n",
        "Numerous hyperparameters – e.g. the learning rate, the total number of samples to train on – influence the learning process and are usually determined by testing some variations."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cIqoV0GSI52v"
      },
      "source": [
        "trade = data_split(processed_full, '2019-01-01','2021-01-01')\n",
        "e_trade_gym = StockTradingEnv(df = trade, turbulence_threshold = 380, **env_kwargs)\n",
        "# env_trade, obs_trade = e_trade_gym.get_sb_env()"
      ],
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309
        },
        "id": "W_XNgGsBMeVw",
        "outputId": "9bd9af7b-9e2b-4c30-bd3d-b1459d189a41"
      },
      "source": [
        "trade.head()"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>tic</th>\n",
              "      <th>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>close</th>\n",
              "      <th>volume</th>\n",
              "      <th>day</th>\n",
              "      <th>macd</th>\n",
              "      <th>boll_ub</th>\n",
              "      <th>boll_lb</th>\n",
              "      <th>rsi_30</th>\n",
              "      <th>cci_30</th>\n",
              "      <th>dx_30</th>\n",
              "      <th>close_30_sma</th>\n",
              "      <th>close_60_sma</th>\n",
              "      <th>turbulence</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2019-01-02</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>38.722500</td>\n",
              "      <td>39.712502</td>\n",
              "      <td>38.557499</td>\n",
              "      <td>38.505024</td>\n",
              "      <td>148158800.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.335191</td>\n",
              "      <td>334.139821</td>\n",
              "      <td>308.258949</td>\n",
              "      <td>47.826590</td>\n",
              "      <td>-27.772146</td>\n",
              "      <td>14.525416</td>\n",
              "      <td>314.202890</td>\n",
              "      <td>312.704339</td>\n",
              "      <td>51.409086</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2019-01-02</td>\n",
              "      <td>AXP</td>\n",
              "      <td>93.910004</td>\n",
              "      <td>96.269997</td>\n",
              "      <td>93.769997</td>\n",
              "      <td>92.319603</td>\n",
              "      <td>4175400.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.775741</td>\n",
              "      <td>335.611436</td>\n",
              "      <td>305.851699</td>\n",
              "      <td>46.156688</td>\n",
              "      <td>-67.860390</td>\n",
              "      <td>22.313462</td>\n",
              "      <td>314.159711</td>\n",
              "      <td>312.579659</td>\n",
              "      <td>51.409086</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2019-01-02</td>\n",
              "      <td>BA</td>\n",
              "      <td>316.190002</td>\n",
              "      <td>323.950012</td>\n",
              "      <td>313.709991</td>\n",
              "      <td>314.645142</td>\n",
              "      <td>3292200.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>-0.338838</td>\n",
              "      <td>336.485023</td>\n",
              "      <td>304.296566</td>\n",
              "      <td>46.806048</td>\n",
              "      <td>-72.067253</td>\n",
              "      <td>24.597730</td>\n",
              "      <td>314.064055</td>\n",
              "      <td>312.452928</td>\n",
              "      <td>51.409086</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2019-01-02</td>\n",
              "      <td>CAT</td>\n",
              "      <td>124.029999</td>\n",
              "      <td>127.879997</td>\n",
              "      <td>123.000000</td>\n",
              "      <td>118.671188</td>\n",
              "      <td>4783200.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>-0.655001</td>\n",
              "      <td>336.668731</td>\n",
              "      <td>303.510025</td>\n",
              "      <td>49.792068</td>\n",
              "      <td>-33.729612</td>\n",
              "      <td>14.054906</td>\n",
              "      <td>314.492846</td>\n",
              "      <td>312.392662</td>\n",
              "      <td>51.409086</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2019-01-02</td>\n",
              "      <td>CSCO</td>\n",
              "      <td>42.279999</td>\n",
              "      <td>43.200001</td>\n",
              "      <td>42.209999</td>\n",
              "      <td>40.057236</td>\n",
              "      <td>23833500.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>-0.159995</td>\n",
              "      <td>336.703986</td>\n",
              "      <td>304.162298</td>\n",
              "      <td>53.389344</td>\n",
              "      <td>31.217425</td>\n",
              "      <td>7.135987</td>\n",
              "      <td>315.392609</td>\n",
              "      <td>312.451774</td>\n",
              "      <td>51.409086</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         date   tic        open  ...  close_30_sma  close_60_sma  turbulence\n",
              "0  2019-01-02  AAPL   38.722500  ...    314.202890    312.704339   51.409086\n",
              "0  2019-01-02   AXP   93.910004  ...    314.159711    312.579659   51.409086\n",
              "0  2019-01-02    BA  316.190002  ...    314.064055    312.452928   51.409086\n",
              "0  2019-01-02   CAT  124.029999  ...    314.492846    312.392662   51.409086\n",
              "0  2019-01-02  CSCO   42.279999  ...    315.392609    312.451774   51.409086\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eLOnL5eYh1jR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f9c2b3bf-cdfb-45f4-dbbf-2451163da1cc"
      },
      "source": [
        "df_account_value, df_actions = DRLAgent.DRL_prediction(\n",
        "    model=trained_sac, \n",
        "    environment = e_trade_gym)"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "hit end!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ERxw3KqLkcP4",
        "outputId": "cc31064c-cfa2-4bcc-ab3e-f7028744e506"
      },
      "source": [
        "df_account_value.shape"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(505, 2)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "2yRkNguY5yvp",
        "outputId": "a691cfc6-75c5-4c25-9f0e-0c7ba8274e66"
      },
      "source": [
        "df_account_value.tail()"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>account_value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>500</th>\n",
              "      <td>2020-12-24</td>\n",
              "      <td>1.461516e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>501</th>\n",
              "      <td>2020-12-28</td>\n",
              "      <td>1.473744e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>502</th>\n",
              "      <td>2020-12-29</td>\n",
              "      <td>1.468521e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>503</th>\n",
              "      <td>2020-12-30</td>\n",
              "      <td>1.476283e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>504</th>\n",
              "      <td>2020-12-31</td>\n",
              "      <td>1.484414e+06</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           date  account_value\n",
              "500  2020-12-24   1.461516e+06\n",
              "501  2020-12-28   1.473744e+06\n",
              "502  2020-12-29   1.468521e+06\n",
              "503  2020-12-30   1.476283e+06\n",
              "504  2020-12-31   1.484414e+06"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "id": "nFlK5hNbWVFk",
        "outputId": "cced6c73-9f91-4838-eb7d-99c9c2b8d48c"
      },
      "source": [
        "df_actions.head()"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>AAPL</th>\n",
              "      <th>AXP</th>\n",
              "      <th>BA</th>\n",
              "      <th>CAT</th>\n",
              "      <th>CSCO</th>\n",
              "      <th>CVX</th>\n",
              "      <th>DD</th>\n",
              "      <th>DIS</th>\n",
              "      <th>GS</th>\n",
              "      <th>HD</th>\n",
              "      <th>IBM</th>\n",
              "      <th>INTC</th>\n",
              "      <th>JNJ</th>\n",
              "      <th>JPM</th>\n",
              "      <th>KO</th>\n",
              "      <th>MCD</th>\n",
              "      <th>MMM</th>\n",
              "      <th>MRK</th>\n",
              "      <th>MSFT</th>\n",
              "      <th>NKE</th>\n",
              "      <th>PFE</th>\n",
              "      <th>PG</th>\n",
              "      <th>RTX</th>\n",
              "      <th>TRV</th>\n",
              "      <th>UNH</th>\n",
              "      <th>V</th>\n",
              "      <th>VZ</th>\n",
              "      <th>WBA</th>\n",
              "      <th>WMT</th>\n",
              "      <th>XOM</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2019-01-02</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>92</td>\n",
              "      <td>90</td>\n",
              "      <td>98</td>\n",
              "      <td>97</td>\n",
              "      <td>44</td>\n",
              "      <td>97</td>\n",
              "      <td>85</td>\n",
              "      <td>97</td>\n",
              "      <td>98</td>\n",
              "      <td>0</td>\n",
              "      <td>84</td>\n",
              "      <td>0</td>\n",
              "      <td>97</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>93</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>22</td>\n",
              "      <td>0</td>\n",
              "      <td>92</td>\n",
              "      <td>58</td>\n",
              "      <td>44</td>\n",
              "      <td>97</td>\n",
              "      <td>97</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-01-03</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>81</td>\n",
              "      <td>60</td>\n",
              "      <td>89</td>\n",
              "      <td>98</td>\n",
              "      <td>93</td>\n",
              "      <td>98</td>\n",
              "      <td>87</td>\n",
              "      <td>99</td>\n",
              "      <td>99</td>\n",
              "      <td>0</td>\n",
              "      <td>78</td>\n",
              "      <td>0</td>\n",
              "      <td>85</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>97</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>24</td>\n",
              "      <td>0</td>\n",
              "      <td>87</td>\n",
              "      <td>65</td>\n",
              "      <td>92</td>\n",
              "      <td>92</td>\n",
              "      <td>73</td>\n",
              "      <td>-12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-01-04</th>\n",
              "      <td>0</td>\n",
              "      <td>78</td>\n",
              "      <td>87</td>\n",
              "      <td>92</td>\n",
              "      <td>99</td>\n",
              "      <td>96</td>\n",
              "      <td>11</td>\n",
              "      <td>97</td>\n",
              "      <td>79</td>\n",
              "      <td>93</td>\n",
              "      <td>97</td>\n",
              "      <td>0</td>\n",
              "      <td>92</td>\n",
              "      <td>0</td>\n",
              "      <td>95</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>98</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-7</td>\n",
              "      <td>0</td>\n",
              "      <td>94</td>\n",
              "      <td>67</td>\n",
              "      <td>-59</td>\n",
              "      <td>96</td>\n",
              "      <td>96</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-01-07</th>\n",
              "      <td>0</td>\n",
              "      <td>-36</td>\n",
              "      <td>94</td>\n",
              "      <td>91</td>\n",
              "      <td>98</td>\n",
              "      <td>96</td>\n",
              "      <td>58</td>\n",
              "      <td>97</td>\n",
              "      <td>7</td>\n",
              "      <td>98</td>\n",
              "      <td>98</td>\n",
              "      <td>0</td>\n",
              "      <td>96</td>\n",
              "      <td>0</td>\n",
              "      <td>90</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>95</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>15</td>\n",
              "      <td>0</td>\n",
              "      <td>90</td>\n",
              "      <td>24</td>\n",
              "      <td>-70</td>\n",
              "      <td>85</td>\n",
              "      <td>98</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-01-08</th>\n",
              "      <td>0</td>\n",
              "      <td>39</td>\n",
              "      <td>95</td>\n",
              "      <td>98</td>\n",
              "      <td>98</td>\n",
              "      <td>99</td>\n",
              "      <td>89</td>\n",
              "      <td>98</td>\n",
              "      <td>-33</td>\n",
              "      <td>99</td>\n",
              "      <td>95</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>0</td>\n",
              "      <td>99</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>96</td>\n",
              "      <td>0</td>\n",
              "      <td>68</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-30</td>\n",
              "      <td>0</td>\n",
              "      <td>93</td>\n",
              "      <td>55</td>\n",
              "      <td>-7</td>\n",
              "      <td>94</td>\n",
              "      <td>99</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            AAPL  AXP  BA  CAT  CSCO  CVX  DD  ...  TRV  UNH   V  VZ  WBA  WMT  XOM\n",
              "date                                           ...                                 \n",
              "2019-01-02     0    0  92   90    98   97  44  ...    0   92  58  44   97   97   12\n",
              "2019-01-03     0    0  81   60    89   98  93  ...    0   87  65  92   92   73  -12\n",
              "2019-01-04     0   78  87   92    99   96  11  ...    0   94  67 -59   96   96    0\n",
              "2019-01-07     0  -36  94   91    98   96  58  ...    0   90  24 -70   85   98    0\n",
              "2019-01-08     0   39  95   98    98   99  89  ...    0   93  55  -7   94   99    0\n",
              "\n",
              "[5 rows x 30 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W6vvNSC6h1jZ"
      },
      "source": [
        "<a id='6'></a>\n",
        "# Part 7: Backtest Our Strategy\n",
        "Backtesting plays a key role in evaluating the performance of a trading strategy. Automated backtesting tool is preferred because it reduces the human error. We usually use the Quantopian pyfolio package to backtest our trading strategies. It is easy to use and consists of various individual plots that provide a comprehensive image of the performance of a trading strategy."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lr2zX7ZxNyFQ"
      },
      "source": [
        "<a id='6.1'></a>\n",
        "## 7.1 BackTestStats\n",
        "pass in df_account_value, this information is stored in env class\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nzkr9yv-AdV_",
        "outputId": "bd56925d-7d32-479a-da1f-8606dff1b0f8"
      },
      "source": [
        "print(\"==============Get Backtest Results===========\")\n",
        "now = datetime.datetime.now().strftime('%Y%m%d-%Hh%M')\n",
        "\n",
        "perf_stats_all = backtest_stats(account_value=df_account_value)\n",
        "perf_stats_all = pd.DataFrame(perf_stats_all)\n",
        "perf_stats_all.to_csv(\"./\"+config.RESULTS_DIR+\"/perf_stats_all_\"+now+'.csv')"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "==============Get Backtest Results===========\n",
            "Annual return          0.217889\n",
            "Cumulative returns     0.484414\n",
            "Annual volatility      0.199162\n",
            "Sharpe ratio           1.091714\n",
            "Calmar ratio           1.142071\n",
            "Stability              0.719261\n",
            "Max drawdown          -0.190784\n",
            "Omega ratio            1.230272\n",
            "Sortino ratio          1.584368\n",
            "Skew                        NaN\n",
            "Kurtosis                    NaN\n",
            "Tail ratio             1.028679\n",
            "Daily value at risk   -0.024229\n",
            "dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QkV-LB66iwhD",
        "outputId": "061ac7da-3c70-4790-cd95-7627855bbe21"
      },
      "source": [
        "#baseline stats\n",
        "print(\"==============Get Baseline Stats===========\")\n",
        "baseline_df = get_baseline(\n",
        "        ticker=\"^DJI\", \n",
        "        start = '2019-01-01',\n",
        "        end = '2021-01-01')\n",
        "\n",
        "stats = backtest_stats(baseline_df, value_col_name = 'close')\n"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "==============Get Baseline Stats===========\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (505, 8)\n",
            "Annual return          0.144674\n",
            "Cumulative returns     0.310981\n",
            "Annual volatility      0.274619\n",
            "Sharpe ratio           0.631418\n",
            "Calmar ratio           0.390102\n",
            "Stability              0.116677\n",
            "Max drawdown          -0.370862\n",
            "Omega ratio            1.149365\n",
            "Sortino ratio          0.870084\n",
            "Skew                        NaN\n",
            "Kurtosis                    NaN\n",
            "Tail ratio             0.860710\n",
            "Daily value at risk   -0.033911\n",
            "dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9U6Suru3h1jc"
      },
      "source": [
        "<a id='6.2'></a>\n",
        "## 7.2 BackTestPlot"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "lKRGftSS7pNM",
        "outputId": "00deee41-f99f-4aaf-a389-e66315de1ff6"
      },
      "source": [
        "print(\"==============Compare to DJIA===========\")\n",
        "%matplotlib inline\n",
        "# S&P 500: ^GSPC\n",
        "# Dow Jones Index: ^DJI\n",
        "# NASDAQ 100: ^NDX\n",
        "backtest_plot(df_account_value, \n",
        "             baseline_ticker = '^DJI', \n",
        "             baseline_start = '2019-01-01',\n",
        "             baseline_end = '2021-01-01')"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "==============Compare to DJIA===========\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (505, 8)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\"><th>Start date</th><td colspan=2>2019-01-02</td></tr>\n",
              "    <tr style=\"text-align: right;\"><th>End date</th><td colspan=2>2020-12-31</td></tr>\n",
              "    <tr style=\"text-align: right;\"><th>Total months</th><td colspan=2>24</td></tr>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Backtest</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Annual return</th>\n",
              "      <td>21.789%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cumulative returns</th>\n",
              "      <td>48.441%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Annual volatility</th>\n",
              "      <td>19.916%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sharpe ratio</th>\n",
              "      <td>1.09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Calmar ratio</th>\n",
              "      <td>1.14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Stability</th>\n",
              "      <td>0.72</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Max drawdown</th>\n",
              "      <td>-19.078%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Omega ratio</th>\n",
              "      <td>1.23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sortino ratio</th>\n",
              "      <td>1.58</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Skew</th>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kurtosis</th>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tail ratio</th>\n",
              "      <td>1.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Daily value at risk</th>\n",
              "      <td>-2.423%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Alpha</th>\n",
              "      <td>0.13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Beta</th>\n",
              "      <td>0.55</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Worst drawdown periods</th>\n",
              "      <th>Net drawdown in %</th>\n",
              "      <th>Peak date</th>\n",
              "      <th>Valley date</th>\n",
              "      <th>Recovery date</th>\n",
              "      <th>Duration</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>19.08</td>\n",
              "      <td>2020-02-12</td>\n",
              "      <td>2020-03-12</td>\n",
              "      <td>2020-06-03</td>\n",
              "      <td>81</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>11.21</td>\n",
              "      <td>2020-06-08</td>\n",
              "      <td>2020-06-26</td>\n",
              "      <td>2020-09-02</td>\n",
              "      <td>63</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>8.72</td>\n",
              "      <td>2020-09-02</td>\n",
              "      <td>2020-10-28</td>\n",
              "      <td>2020-11-09</td>\n",
              "      <td>49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>7.13</td>\n",
              "      <td>2019-07-15</td>\n",
              "      <td>2019-08-14</td>\n",
              "      <td>2019-11-01</td>\n",
              "      <td>80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.02</td>\n",
              "      <td>2019-02-22</td>\n",
              "      <td>2019-06-03</td>\n",
              "      <td>2019-06-17</td>\n",
              "      <td>82</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/pyfolio/tears.py:907: UserWarning: Passed returns do not overlap with anyinteresting times.\n",
            "  'interesting times.', UserWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1008x5184 with 13 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BzBaE63H3RLc"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}